AI Innovation Institute

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The current COVID-19 situation is challenging and difficult for all of us - we hope this virtual conference will allow colleagues to share and discuss their latest research, while under travel and stay-at-home restrictions.

Because of the ongoing COVID-19/SARS-CoV-2 outbreak, CSHL and the organizers have now reached the difficult decision to restructure the meeting on From Neuroscience to Artificially Intelligent Systems into a virtual meeting scheduled November 9-12, 2020.  NAISys will begin at 10 am (EDT)  on Monday, November 9 and ending with an afternoon session on Thursday, November 12, 2020. Oral sessions will be confined to later morning and afternoon sessions EST to maximize access by participants from around the world. 

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Artificial intelligence (AI) and neural networks have long drawn on neuroscience for inspiration. However, in spite of tremendous recent advances in AI, natural intelligence is still far more adept at interacting with the real world in real-time, adapting to changes, and doing so under significant physical and energetic constraints. The goal of this meeting is to bring together researchers at the intersection of AI and neuroscience, and to identify insights from neuroscience that can help catalyze the development of next-generation artificial systems.

Abstracts are welcomed on all scientific topics related to how principles and insights from neuroscience can lead to better artificial intelligence. Topics of interest include but are not limited to network architectures, computing with spiking networks, learning algorithms, active perception, inductive bias, meta-learning, and online learning. Please note that abstracts should be ONE page (~2900 characters).   

New and revised abstracts should be submitted by the resubmission deadline, Friday, August 14. Individuals originally selected for talks should assume they will still be speaking, but we may select additional talks based on the number of invited and selected speakers who cannot reconfirm.

Abstracts should contain only new and unpublished material and must be submitted electronically by the abstract deadline. Selection of material for oral and poster presentation will be made by the organizers and individual session chairs. Status (talk/poster) of abstracts will be posted on our web site as soon as decisions have been made by the organizers.

We are eager to have as many students and postdocs as possible to attend since they are likely to benefit most from this meeting. We have applied for funds from industry and foundations to partially support graduate students and postdocs. Apply in writing stating need for financial support to Catie Carr at carr@cshl.edu. Preference is given to those submitting abstracts. 

All questions pertaining to registration, fees, abstract submission or any other matters may be directed to Catie Carr at carr@cshl.edu.

We anticipate the following support :

Social Media:

The designated hashtag for this meeting is #cshlNeuroAI. Note that you must obtain permission from an individual presenter before live-tweeting or discussing his/her talk, poster, or research results on social media. Click the Policies tab above to see our full Confidentiality & Reporting Policy.

Virtual Academic Package: $285
Virtual Graduate Student Package: $175
Virtual Corporate Package: $425

Lab Group Discounts (not departmental or institutional discounts):

Labs registering 4 people: 20% discount off applicable fees
Labs registering 5 people: 25% discount off applicable fees
Labs registering 6 people: 30% discount off applicable fees

To be eligible for lab group discounts, please submit a list of lab members planning to attend in advance of registration to Catie Carr  to establish appropriate discounted fees. Please include a link to your lab web page for verification purposes. Prior payments will be included in the group discount calculation.

IBRO/International Brain Research Organization are generously supporting the participation of a limited number of individuals from US/Canadian Minority Serving Institutions (check eligibility): $25
Limit: 65 attendees / limit per institution: 5 (contact Catie Carr  to confirm eligibility/availability prior to registering) 

Reduced Pricing for Individuals from US/Canadian Minority Serving Institutions (check eligibility): $50

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.

Abstract: This presentation will begin by outlining key challenges facing the modern power grid and summarizing our group's research efforts to address them. It will then discuss how AI and machine learning are reshaping the grid modernization. The major focus of the talk will highlight a range of AI/ML applications we have developed in recent years to enhance grid operation, planning, control, and security.

Biography: Meng Yue is currently leading the Grid Modernization and Security Group in the Interdisciplinary Science Department at Brookhaven National Laboratory (BNL). He received his Ph. D. from Michigan State University in electrical engineering. His major research interests include power system modeling, simulation, and control, and applications of AI/ML- and quantum machine learning and quantum computing in operation, planning, and security of the future grid.

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.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support

Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.

Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.