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Events for December

  • AI SEMINAR - Towards a computational framework for how we represent other people

    Fri, Dec 05, 2014 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Damian Stanley, Caltech

    Talk Title: Towards a computational framework for how we represent other people

    Abstract: Predicting other peoples’ beliefs, desires, and intentions is a primary function of human cognition and is essential to thrive in our complex social world. To do this efficiently and successfully, we must form lasting representations of individuals and social groups based on information we receive through personal and vicarious experience. My research is focused on developing a computational account of the neurocognitive mechanisms through which we learn about other people, make social predictions, and are influenced by social biases. To achieve this, I employ a multidisciplinary approach, integrating a wide range of techniques from cognitive neuroscience, social psychology, neuroeconomics, computational modeling of learning and decision-making, and clinical psychology. My theoretical model of social learning and decision-making treats social group biases as a set of initial guesses (akin to Bayesian priors) that inform our social decision-making when we lack specific information about a person with whom we are interacting. Using these priors as a starting point, we form and update our mental representation of a person (as well as their social group) on the basis of observed behavior. I will present behavioral and neural data on the influence of race bias on trust estimations, as well as the computational processes through which we learn about individuals’ traits and intentions (i.e., theory of mind), and how these processes might be disrupted in individuals with social impairments (e.g. Autism Spectrum Disorder). These results suggest that while many common processes support learning about social and non-social entities, there may also exist neural computations unique to social learning.

    Biography: Damian Stanley completed his Ph.D. in Neural Science at New York University in 2005, studying mid-level visual processing. In his postdoctoral work, he turned his focus toward developing a computational account of the neurocognitive processesthrough which we learn about and represent other people. In his first postdoctoral position with Elizabeth Phelps at New York University he investigated how implicit race biases influence social trust. In his current postdoctoral position, with Drs. Ralph Adolphs and John O’Doherty at Caltech, he uses computational models and model-based fMRI to characterize typical and atypical (e.g. autism spectrum disorder) social learning. This line of research is funded by an NIMH career development award (K01-MH099343).

    Host: Greg Ver Steeg

    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=8d563808c16942bda353a815b33370d01

    Location: Information Science Institute (ISI) - 11th floor conference room

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=8d563808c16942bda353a815b33370d01d

    Audiences: Everyone Is Invited

    Posted By: Kary LAU

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  • NL Seminar- Multisensory integration in a neural framework for concepts

    Fri, Dec 05, 2014 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Kingson Man , USC/BCI

    Talk Title: Multisensory integration in a neural framework for concepts

    Series: Natural Language Seminar

    Abstract: How are concepts represented in the brain? When we hear the ringing of a bell, or watch a bell swinging back and forth, is there a shared "BELL" pattern of neural activity in our brains? Philosophers have debated the nature of concepts for centuries, but recent technical advances have allowed neuroscientists to make contributions to this topic. The combination of functional neuroimaging and machine learning has allowed us to examine distributed patterns of activity in the human brain to decode what they represent about the world, and to what level of abstraction. I describe our recent findings that revealed a hierarchical organization of multisensory information integration, leading to representations that generalize across different sensory modalities. I will also discuss our work on the social function of concepts, which enables the communication of similar thoughts and associations between individuals.



    Biography: I am a research associate at the Brain and Creativity Institute of the University of Southern California. I earned my Ph.D. at USC, mentored by Antonio Damasio. I am interested in the general problem of consciousness, and in particular how different sensations are bound together by the brain into a unified experience of the world.

    Host: Aliya Deri and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/
    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=56056025433c402fa77a297e7b2e24381

    Location: Information Science Institute (ISI) - 6th Flr Conf Rm # 689, Marina Del Rey

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=56056025433c402fa77a297e7b2e24381d

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • AI Seminar- Uncovering meaning construction and representation in the reading brain

    Mon, Dec 15, 2014 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Leila Wehbe , CMU

    Talk Title: Uncovering meaning construction and representation in the reading brain

    Series: Artificial Intelligence Seminar

    Abstract: How is information organized in the brain when it reads? Where and when do the required processes occur, such as perceiving the individual words, combining them with the previous words and maintaining a representation of the overall meaning?

    I will present results from a recent experiment in which we align context-based neural network language models and brain activity during reading. When processing a text word by word, both the brain and the neural networks perform the same processes. They both maintain a representation for the previous context. They both represent the properties of the incoming word and then integrate it with context. We study the alignment between the latent vectors used by these neural networks and the brain activity observed via Magnetoencephalography (MEG) when subjects read a chapter from Harry Potter and the Sorcerer’s Stone. For that purpose we apply the neural network to the same chapter the subjects are reading, and explore the ability of these vector representations to predict the observed word-by-word brain activity.

    Our novel results include a suggested time-line of how the brain updates its representation of context. They also demonstrate the incremental perception of every new word starting early in the visual cortex, moving next to the temporal lobes and finally to the frontal regions. Furthermore, the results suggest the integration process occurs in the temporal lobes after the new word has been perceived.

    This is joint work with Ashish Vaswani, Kevin Knight and Tom Mitchell, and is a part of a larger effort to understand how the brain organizes information in natural reading. I will describe this research direction. I will also mention results from a sister experiment in which we demonstrate how the brain areas involved in reading are processing different types of information (such as syntax, semantics or narrative information). This second experiment is joint work with Brian Murphy, Partha Talukdar, Alona Fyshe, Aaditya Ramdas and Tom Mitchell. Finally I will also mention some of our past and upcoming machine learning projects that aim to improve our methodological pipeline.







    Biography: Leila is a PhD student supervised by Professor Tom Mitchell in the Machine Learning Department at Carnegie Mellon University. She is part of the dual-track program in the Center for the Neural Basis of Cognition. She received her BE in Electrical and Computer Engineering from the American University of Beirut.

    HomePage Link:
    http://www.cs.cmu.edu/ lwehbe/

    Host: Kevin Knight

    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=e1bbe0a29af44690aa0eb5ae9c9f93081

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=e1bbe0a29af44690aa0eb5ae9c9f93081d

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • AI Seminar: Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic

    Thu, Dec 18, 2014 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Raymond Mooney, Professor at CS Dept, University of Texas at Austin

    Talk Title: Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic

    Series: AISeminar

    Abstract: 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.

    Biography: Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 150 published research papers, primarily in the areas of machine learning and natural language processing. He was the President of the International Machine Learning Society from 2008-2011, program co-chair for AAAI 2006, general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery, and the recipient of best paper awards from AAAI-96, KDD-04, ICML-05 and ACL-07.

    Host: Ulf Hermjakob

    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=408d23eb190243b4ad3717c19c0b0f461

    Location: Information Science Institute (ISI) - 1135

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=408d23eb190243b4ad3717c19c0b0f461d

    Audiences: Everyone Is Invited

    Posted By: Alma Nava / Information Sciences Institute

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