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.

Speakers

Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division

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

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

Meeting ID: 161 528 9117
Passcode: 991382

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.

ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.


For more information and registration, visit the official website.
AI Seminar: Video Architecture Search - Michael Ryoo Abstract: Video understanding is a challenging problem. Because a video contains spatio-temporal data, its feature representation is required to abstract both appearance and motion information. This is not only essential for automated understanding of the semantic content of videos, such as Web-video classification or sport activity recognition, but is also crucial for robot perception and learning. Previously, convolutional neural networks (CNNs) for videos were normally built by manually extending known 2D architectures such as Inception and ResNet to 3D or by carefully designing two-stream CNN architectures that fuse together both appearance and motion information. However, designing an optimal video architecture to best take advantage of spatio-temporal information in videos still remains an open problem. In this talk, we discuss recent progress in neural architecture search for videos, obtaining more optimal network architectures for video understanding.
Abstract: As computing and society become increasingly inseparable, we confront a fundamental design challenge: creating AI systems where human-machine interactions authentically embody our diverse values while thoughtfully evolving our social relationships. The recursive nature of these interactions--where human behavior shapes technology design and technological affordances influence human behavior--presents both profound risks and transformative opportunities as we reimagine our collective digital future. What interaction patterns emerge when algorithmic systems become active participants in societal decision-making? How can we design human-AI collaboration that ensures algorithmic systems align with diverse community values while serving the public interest? Through Public Interest AI, we explore a Pluralistic Design Language that creates interaction models for value-sensitive algorithmic ecosystems, strengthening AI-society alignment in both technology design and policy development. Through collaborative interaction with communities, we create systems that augment human capabilities while embedding ethical principles into the sociotechnical design of AI itself--ultimately redefining possibilities at the intersection of technology, policy, and society. This talk will examine the challenges of designing meaningful human-AI systems within social contexts through real-world applications that combine value-sensitive interaction design, human-inspired computing, and societal development to create technologies that advance our shared commitment to the public good.

Bio: Neil Gaikwad is an Assistant Professor of Data Science and Computer Science at UNC Chapel Hill. Additionally, he serves on the Faculty Advisory Council of the UNC Parr Center for Ethics and is a Fellow at the MIT Dalai Lama Center for Ethics and Transformative Values. Neil holds a Ph.D. in Society-Centered AI from MIT and is an alumnus of Carnegie Mellon University's School of Computer Science. Neil's scholarship, published in prominent AI and HCI conferences, has been recognized with several prestigious honors, including the Facebook Research Fellowship, UIST Best Paper Honorable Mention, MIT Engineering Fellowship, Human Rights & Technology Fellowship, Graduate Teaching Award, and the Karl Taylor Compton Prize, MIT's highest student honor. He has been recognized as a Rising Star by both Stanford University and the University of Chicago. Translating research into real-world impact, Neil is a dedicated educator and mentor who has taught over 500 students throughout his career. He has guided more than 30 students to publish influential papers on AI fairness, secure prestigious fellowships, and contribute to shaping AI policy through public interest research. Neil is also the founder of the AI Policy Global Initiative, which has successfully brought together academia, industry, government, and communities to address critical challenges in AI governance and develop collaborative approaches to responsible AI.

Location: Old Computer Science, room 1310

AI is everywhere -- and so are the privacy concerns that come with it. At its core, the most common forms of AI we use today are online digital services -- and thus inherit the usual privacy risks of any internet-based tool. However, AI also introduces a set of unique and evolving risks. We'll take a closer look at one of the newest developments in this area: indirect prompt injection -- a technique that can trick AI tools into revealing or extracting private information. You'll learn how this emerging form of AI manipulation works, why it matters, and how to protect yourself -- as well as how similar techniques are being used in academic contexts to manipulate systems and even mislead researchers.

Register for this Zoom workshop.

Are you interested in understanding the challenges that lie ahead as Artificial Intelligence (AI) systems become increasingly autonomous, dynamically acquire information, and adapt behaviors?
 
Join us for an exciting afternoon of talks by visionaries and leaders from industry, government, and academia as we kickoff a three-part Trusted AI Challenge Series designed to Build the Vision - Formalize Challenges - Advance the Art of next generation of AI systems.
 
The Air Force Research Laboratory Information Directorate, The State University of New York, Innovare Advancement Center, NYSTEC, and Griffiss Institute invite you to join us for this half-day virtual event!
 
WHEN: Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT
 
Hosted by Innovare Advancement Center, this webinar is the first of a three-part series designed to cultivate, define and fund creative solutions to a set of challenge problems in trustworthy AI with a particular focus on dynamic, autonomous systems that learn and adapt behaviors.
 
Keynote speakers include Dr. David Goldstein of  Space X; Dr. Scott Hubbard of Stanford University; Dr. Pramod Khargonekar of UC Irvine, and more!
 
This event is designed for academic and government researchers, university students, and small businesses.
 
Would you like to understand some of the most formidable technical challenges in future autonomous systems?  Would you like to sponsor some of the brightest minds in AI to work on problems of interest to you? Would you like to learn more about AI in real systems?
 
If so, Save the Date! Wednesday, October 14, 2020, 12:00 PM - 4:00 PM EDT.
 
Please see additional information on the three-part series here. Registration details to follow! 
 
Stay tuned: https://www.innovare.org/news-events  
How do you get the most out of generative AI? Stop by the library Galleria outside of the Central Reading Room to learn more! Librarians Chris Kretz and Ahmad Pratama, along with David Ecker of DoIT, will be demonstrating tools and tips for writing prompts that make the most of what AI can do. And they'll be hosting Explore AI demos this Monday - Wednesday (March 3rd-5th) 12:30 - 1:30. Whether you're new to AI or a current user, they'd love to talk to you about it.

Location: Melville Library Galleria