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.
AI and Edge Processing Co-Design for Radiation Detectors
Abstract: Artificial Intelligence (AI) offers exciting new opportunities for enhancing the performance of radiation detectors, ultimately leading to improved physics outcomes. Furthermore, with the explosive growth in data rates being seen by next-generation radiation detectors, deployment of AI algorithms at the edge by embedding intelligence within or near the detector front-end can be transformative. Such integration enables real-time data filtering, noise suppression, feature extraction, and adaptive control, while reducing downstream bandwidth and power consumption. This talk will cover three efforts that bring AI to the forefront of detector technology. First, we demonstrate how AI-based algorithms can be used for position reconstruction in virtual Frisch-grid (VFG) detectors by compensating for charge transport distortions and detector non- uniformities, leading to significantly enhanced fidelity in imaging of gamma-ray interactions. Second, we present a smart readout application specific integrated circuit (ASIC) that combines digital signal processing with co-designed artificial neural networks to enable on-chip regression and classification of detector signals, while meeting stringent constraints on accuracy, speed, and area. Finally, we introduce our recent efforts related to the development of electro-photonic processing architectures that integrate CMOS electronics and silicon photonics for near-sensor AI acceleration. These architectures aim to leverage cross-disciplinary co-design from algorithms to hardware, to achieve low latency and energy-efficient processing of detector data.
Biography: Dr. Prashansa Mukim is an early-career researcher in the Instrumentation Department at BNL, where she works on the design of front-end electronics for extreme environments and the development of co-design methodologies for novel processing modalities and beyond-CMOS technologies. Prior to joining BNL, she was a post-doctoral researcher at the National Institute of Standards and Technology (NIST) in Maryland, where she focused on characterizing the properties of CMOS circuits at cryogenic temperatures and applications of spintronic devices for neuromorphic computing. She received her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2021.
Location: CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1608585935?pwd=UemgEkqijfNf3vIJIGuOa2MdjsunaT.1
Meeting ID: 160 858 5935
Passcode: 076033
Event Description: This interdisciplinary symposium covers the application of artificial intelligence (AI) throughout the entire life cycle of new materials -- from materials simulations and synthesis to translating research into high-volume industrial production.
Event Link & Registration: nyas.org/AI4Materials2020
Time: Mar 17, 2021 10:00 AM Eastern Time (US and Canada)
Join Zoom Meeting
https://stonybrook.zoom.us/j/
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.
Th 3.30-6.20, Old Computer Science Building - CS2311, Zoom option
Instructor: Owen Rambow
Pragmatics is the study of how context (linguistic and non-linguistic) affects language use: the utterer (speaker or writer) chooses among many linguistic means licensed by the grammar of the language. Core explanatory notions in pragmatics are the common ground between utterer and addressee, and the use of speech acts to change the common ground.
This seminar will investigate the proposal of Grice, which has been called intentionalism. According to Grice, an utterer forms a communicative intention, performs a speech act to achieve the intention, and the speech act succeeds when the addressee recognizes the intention. The result is a change in common ground. With intentionalism, the notion of cognitive state becomes central to pragmatic theory. We will investigate Gricean intentionalism, including formalizations such as Stalnaker's, empirical evidence from cognitive science, and alternate pragmatic theories. In the second part of the course, we will then examine specific issues in pragmatics in light of the foundational theories we have discussed, including information structure, specific linguistic issues such as discourse particles, intonation, and politeness, and neuro-divergent communication. We will pay attention to empirical evidence throughout the course.
You can enroll for 0-3 credits, and further topics of interest may be included.
List of papers: https://protect.checkpoint.com
AI Companion is a meeting summary tool that can capture and summarize what is said in a Zoom meeting transcript, eliminating the need for a notetaker. In addition, meeting participants can use AI Companion to ask questions to a chat bot to get clarity on information within a meeting, and they can also use it to create stylistic virtual backgrounds.
Register here for the online session