Google Docs has been updated to integration Gemini AI. Learn about the integrations that gives you more features within Google Docs. There are also AI Automations that will keep you updated when things happen with documents.

In this session, you will:

  1. Learn about the AI features within Google Docs
  2. Learn about workflow to be notified of changes to Docs
  3. Understand the features that allow AI to work

Register here.
Title: Cultural Biases, World Languages, and User Privacy in Large Language Models
Abstract: In this talk, I will highlight three key aspects of large language models: (1) cultural bias in LLMs and pre-training data, (2) decoding algorithm for low-resource languages, and (3) human-centered design for real-world applications.

The first part focuses on systematically assessing LLMs' favoritism towards Western culture. We take an entity-centric approach to measure the cultural biases among LLMs (e.g., GPT-4, Aya, and mT5) through natural prompts, story generation, sentiment analysis, and named entity tasks. One interesting finding is that a potential cause of cultural biases in LLMs is the extensive use and upsampling of Wikipedia data during the pre-training of almost all LLMs. The second part will introduce a constrained decoding algorithm that can facilitate the generation of high-quality synthetic training data for fine-grained prediction tasks (e.g., named entity recognition, event extraction). This approach outperforms GPT-4 on many non-English languages, particularly low-resource African languages. Lastly, I will showcase an LLM-powered privacy preservation tool designed to safeguard users against the disclosure of personal information. I will share findings from an HCI user study that involves real Reddit users utilizing our tool, which in turn informs our ongoing efforts to improve the design of AI models.
Bio:

Wei Xu is an Associate Professor in the College of Computing and Machine Learning Center at the Georgia Institute of Technology, where she is the director of the NLP X Lab. Her research interests are in natural language processing and machine learning, with a focus on Generative AI, robustness and fairness of large language models, multilingual LLMs, as well as AI for science, education, accessibility, and privacy research. She is a recipient of the NSF CAREER Award, Google Academic Research Award, CrowdFlower AI for Everyone Award, Best Paper Awards and Honorable Mentions at COLING'18, ACL'23, ACL'24. She also received research funds from DARPA and IARPA. She is currently an executive board member of NAACL. Join Zoom Meeting https://stonybrook.zoom.us/j/98855994362?pwd=F2qnpwL85fhCBHAEW9ZBpXihfwGHsj.1 (ID: 98855994362, passcode: 172797) Join by phone (US) +1 646-876-9923 (passcode: 172797) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DuDJcUTvyQueZkCaUSAwFlg%253D%253D%26signature%3Da3d49e0f7f2e74e7130f7308c74bd85ba7b99587b98ba2e34238bb657ca51a09%26v%3D1&sa=D&source=calendar&usg=AOvVaw2jTn5cjfRG8vXU8KHHlU2Y Meeting host: H.Andrew.Schwartz@stonybrook.edu

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What can you learn from over seven years' worth of Twitter bios? Steven Skiena, Distinguished Teaching Professor of Computer Science and Director of SBU's Institute for AI-Driven Discovery and Innovation, will tell us.

Presenting work done with collaborators Jason Jones, Dakota Handzlik, and Xingzhi Guo, Dr. Skiena will discuss what the team learned about how people portray themselves on social media through their political identities and job status. He'll also show us what you can predict about a person based on their self-description.

If you have a disability and are requesting accommodations in order to fully participate in this event, please email libraryevents@stonybrook.edu or call 631-632-7100.

Register now: https://library.stonybrook.edu/library-events/stem-speaker-series-measuring-self-identity/

AI/ML Working Group Seminar

Time/Date: 12:00 PM ET, Tuesday, March 1st, 2022

Seminar Speaker: Yen-Chi (Sam) Chen, CSI, Brookhaven National Laboratory

Title: When reinforcement learning meets quantum computing

Abstract: Recently, reinforcement learning (RL) has demonstrated
various applications with superhuman performance such as mastering the
game of Go.  Meanwhile, the development of quantum computing hardware
shed light on building practical quantum applications to tackle
previously unsolved problems. What will happen if we combine these two
fascinating techniques? In this talk, I will present the recent
progress in quantum RL as well as using classical RL to help certain
tasks in quantum computing.



Host: Meifeng Lin, Computational Science Initiative

_______________________________________________

Nicole Medaglia is inviting you to a scheduled ZoomGov meeting.

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Abstract: The capacity to adapt machine learning models to various contexts, information, and objectives is particularly valuable. In this thesis, I focus on developing Class Conditional Guided Models. These are models that can be adaptively biased towards a class of interest via a conditional input. My primary focus lies in the efficiency of these models. They are constructed to require training only once, with the ability to quickly and conveniently adapt during testing time without necessitating fine-tuning or retraining.
Firstly, I propose RelationVAE, a novel generative model designed for few-shot scenarios, utilizing the prior knowledge of class similarity relationships. RelationVAE is designed to condition on the embeddings of the neighbor classes (i.e. classes with similarity relationships), to generate more reliable samples by making them more similar to the neighbor class. This enables adaptation of the generative model to the provided prior knowledge about class relationships.
As a second focus, I introduce scGAN, a shadow segmentation technique that enables adaptation to varying shadow distributions in different testing environments. scGAN is designed to condition on a sensitivity parameter, a scalar, to control the amount of the shadow detected. In the testing phase, the parameter is set to appropriate values, allowing the model to quickly adapt to specific test environments.
In my third contribution, I propose S-SEG, a methodology for fine-grained counting allowing adaptation to different granularities of fine-grained classes. In fine-grained problems, the distinction between classes is subtle and inconsistent across images, leading to variations in the granularity of the target class from one image to another. S-SEG is designed to be conditioned on an additional input, the sensitivity parameter, to control the granularities of the target class during inference.
My fourth contribution is a text-to-image synthesis method which allows controlling the number of the generated objects of a target class. I propose to generate an intermediate condition, the density map, which reflects the number of objects, together with their layout. This intermediate condition is used to effectively guide the generative model to generate objects with accurate counts.

Speaker: Vu Nguyen

Zoom: https://stonybrook.zoom.us/j/97114455337?pwd=Z4rB9dWcstlahUIs8PRrvQ9b2ZK2Df.1
Meeting ID: 971 1445 5337
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You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room


Speakers

Sanket Jantre
Tao Zhang
Xi Yu


Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

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The Renaissance School Of Medicine Department of Scientific Affairs and its Single Cell Genomics facility are excited to host a special seminar and discussion on AI and single cell genomics analysis:

With the decreasing cost of sequencing, many biobanks and large research cohorts have moved to whole genome sequencing (WGS) and single-cell RNA-seq. However, making use of this deluge of data remains a challenge. I will discuss statistical and deep learning approaches that we are exploring to address the challenge of noncoding variant interpretation, including our work as part of the Alzheimer's disease sequencing project.

Speaker: David A. Knowles, PhD. Asst. Professor of Computer Science, Interdisciplinary Appointee in Systems Biology, Columbia University Core Faculty Member, New York Genome Center

Join us in person: Health Science Tower Level 3, Lecture Hall 5
University Libraries Present: AI as Author? New Considerations When Evaluating Sources.
In this workshop, librarian Christine Fena will review some ways AI is being integrated into published work within the worlds of news and scholarly publication, and discuss how this might impact how to evaluate and understand sources during the research process.
10/2 12:30-1:30 pm on Zoom.
Register via link: https://stonybrook.campuslabs.com/engage/event/10460202
Abstract: Artificial Intelligence (AI) is no longer a futuristic concept -- it is here, but its development, benefits, and risks remain unevenly distributed across industries, nations, and social groups. In this talk, Jieshu presents her research on the societal dimensions of AI from two perspectives: the forces shaping AI's development (backward-looking) and its current and potential impact on society (forward-looking). She first examines disparities in AI, including women's underrepresentation in AI patents and the geographic concentration of AI innovation, highlighting inequalities in who creates AI and who benefits from it. She then explores AI's societal impact, focusing on workforce transformation and the need for GenAI literacy. She will also discuss AI patents, AI's role in climate change mitigation and adaptation, potential environmental biases in LLMs, and gender-specific patterns in AI portrayals in science fiction.

Bio: Jieshu Wang is a Postdoctoral Research Scholar at Arizona State University (ASU), focusing on the social dimensions of artificial intelligence (AI). With a background in engineering, economics, communication, and science and technology studies, she examines how AI both shapes and is shaped by broader societal forces. Her research employs interdisciplinary methods to explore the social, political, and economic factors influencing AI development, as well as its role in innovation, the economy, the future of work, climate change mitigation, and popular culture. Jieshu holds a Ph.D. in Human and Social Dimensions of Science and Technology from ASU. She is also a science book translator and has translated six books.

Location: Old Computer Science, room 1310

From instant question generation to automated data summaries, learn how to leverage Gemini to simplify your entire Google Forms workflow.

Discover how Gemini can now work in Google Forms. Start by creating a whole Google Form from a prompt. Then, watch the real magic after the data is in: We'll dive into Gemini's ability to analyze and summarize open-ended responses, turning a mountain of feedback into a concise, actionable information. Whether you're gathering student feedback or organizing departmental requests, this session will show you how to let AI do the busy work so you can focus on the results.

In this session, you will:

  1. Generate a Google Form from a prompt
  2. Get question suggestions
  3. Summarize text responses with AI

Register here.