Topic: AI Seminar: Owen Rambow
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
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https://stonybrook.zoom.us/j/93614644178?pwd=MzJtVDJYYmU5T1dtMzJiUFMxb0x4dz09
Meeting ID: 936 1464 4178.    Passcode: 965936






Natural Language Understanding and Semantic Parsing

(Partly joint work with former colleagues at Elemental Cognition)

Semantic parsing refers to the task of determining the propositional content of language: who did what to whom.  It is part of the larger task of natural language understanding (NLU).  I will start out by discussing what full NLU means, and argue that we are still far away, as a field, from solving full NLU, or even from knowing how to evaluate it.

In the second part of the talk, I will situate semantic parsing in the context of several other NLU subtasks.  Typically, the target representation of semantic parsing uses an ontology (such as PropBank or FrameNet).  Semantic parsing includes the subtasks of word sense disambiguation, argument detection, and argument role labeling.  I will discuss choices among possible target ontologies.  I will justify why we created a new ontology, Hector, based on FrameNet and the lexical resource NOAD, and explain some of its characteristics.

In the third part of the talk, I will present experiments we performed using transformer models.  We obtain best results using a two-phase model, in which we first choose the frame, and then, given the frame, choose the arguments.  We encode the problem for both tasks using indices in the sentence.  While we develop the parser for our new ontology Hector, this approach also beats the state of the art for FrameNet and PropBank parsing.Biography:  I am a professor in the Department of Linguistics at Stony Brook University with a joint appointment in IACS.

Until recently, I was a research scientist at Elemental Cognition. Elemental Cognition is working on deep natural language understanding.

I got my PhD with Aravind Joshi at the University of Pennsylvania in 1994. I have worked at CoGenTex, and at AT&T Labs -- Research, and for many years I was a research scientist at Columbia University in the Center for Computational Learning Systems.

The INS (International Neuroethics Society) AI and Consciousness Affinity Group is hosting a talk titled Bringing Trustworthiness in Generative AI and Agentic AI Using Thought Knowledge Graphs featuring speaker Manas Gaur, a computer scientist at UMBC.
The talk will examine the interplay between Thought Knowledge Graphs (TKGs) and how they can form more trustworthy and reasoning-based responses in AI. They will also discuss introducing novel methods on implementing TKGs and their overall impact on creating more trustworthy AI systems.
The talk will be held online via Zoom on Monday, December 2 at 1:00pm (EST).
Register to attend.
The Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026) is organized by the ACL Special Interest Group on Arabic NLP (SIGARAB).
The research focus of ArabicNLP is, naturally, Arabic, a collection of language varieties, from Classical to Modern Standard Arabic (MSA), and including many living and historical Arabic dialects. Arabic poses many challenges for the field of computational linguistics, including rich morphology, orthographic ambiguity as well as the wide variety of understudied dialects.

Location: Budapest, Hungary

Register here.
Abstract: Datalog is a powerful language for expressing recursive computations through rules: Horn clauses in first order logic. Although effective at expressing queries over existential properties, Datalog and many of its popular implementations struggle with queries that involve more complex aggregates, requiring users to apply verbose, non-composable, and/or inefficient workarounds. Recent work on lattice-based datalogs addresses many of these concerns for aggregates that can be encoded as lattices (e.g., min or max), but more general aggregates like count remain problematic. In this talk, I will argue that this is not a fundamental limitation of Datalog, but rather from its model of truth: Both datalog semantics and evaluation rules make heavy use of the fact that insertion is both monotone and idempotent. Once a fact is known to be true, it can not be retracted, nor can further discoveries of the same fact alter its truth. Monotonicity is critical for forward progress under Datalog's ``open world'' model, as it allows us to safely assert the truth of a body. Meanwhile, idempotence makes it easier to reason about evaluation, as we need only guarantee that each head atom will be derived at-least-once. Unfortunately, more general aggregates like sum() are neither idempotent, nor monotone. I will introduce Hedgelog, a strict generalization of Datalog that uses general monoids as a basis for truth. I will show that this generalization remains compatible with Datalog's open world model, how it enables cleaner and more composable datalog programs, and how the underlying monoid relations open the door to interesting datastructure-level optimizations.

Bio: Oliver Kennedy is an associate professor at the University at Buffalo. He earned his PhD from Cornell University in 2011 and now leads the Online Data Interactions (ODIn) lab, which operates at the intersection of databases and programming languages. Oliver is the recipient of an NSF CAREER award, an IEEE Region 1 Technological Innovation Award, UB's Exceptional Scholar Award, and several UB SEAS teaching awards. Oliver is also one of the founding board members of Breadcrumb Analytics. Several of Oliver's papers have been invited to Best of compilations from SIGMOD and VLDB. The ODIn lab is currently exploring (i) how we can leverage database techniques like incremental view maintenance to make compilers faster, (ii) how to make it easier for data scientists to track how sources of uncertainty, ambiguity, and/or bias affect analyses, and (iii) how to streamline the interfaces --- both human and software --- between different tools for data science, like python, sql, and spreadsheets.

Location: NCS 120

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Embodied Intelligence at Scientific User Facilities

Abstract: This presentation explores the active work integrating artificial intelligence and robotics at the National Synchrotron Light Source II, and a perspective for the future. Through various case studies, we highlight the optimization of operations, improved experimental outcomes, and the orchestration of distributed multimodal experiments. This ongoing development includes collaborators from across the light and neutron sources in the DOE complex. We will elaborate on the open-source Bluesky project, and its capabilities to support adaptive and autonomous experiments. Additionally, we will discuss how Bluesky can be integrated with open-source robotic control software to unlock new flexible automation for autonomous scientific research, which scales to new experiments and continues to leverage human ingenuity.

Biography: Dr. Phillip M. Maffettone is an Associate Computational Scientist in the Data Science and Systems Integration Division at NSLS-II. His research focuses on accelerating scientific discovery at user facilities through the integration of robotics, artificial intelligence (AI), and advanced experiment orchestration systems. He leads the N3XTware project, constructing the software architecture for the next 12 beamlines to be built at NSLS-II. Prior to this he built the brain on the world's first mobile robotic scientist at the University of Liverpool, and later spearheaded the machine learning platform for a biotechnology start-up, BigHat Biosciences. He holds a DPhil in Inorganic Chemistry from the University of Oxford and a B.S. in Chemical Engineering from the University at Buffalo.

Location: CDS, Bldg. 725, Training Room

Link: https://bnl.zoomgov.com/j/16049713 31?pwd=nc5CV3cOFrdYxordFieP W07tIDmwYb.1

Meeting ID: 160 497 1331
Passcode: 289875

University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.