[NYC AI Event Picks] January Picks! Welcome to 2024!
PioneeringMinds.AI
We aim to curate high-quality events for anyone enthusiastic about AI! We do the heavy lifting for you, so you can find what you need with ease! Event details can be found after the overview.
Check out our website https://pioneeringminds.ai/ for more!
TOP Events Overview
Application Series: Leveraging AI
Jan.10 19:00-20:30 EST | Lewis Johnson: How to Harness Generative AI to Transform Human Learning. Hosted by New York Society for Ethical Culture. In-Person/Zoom RSVP.
Jan.16 19:30-22:30 EST | NYC AI Users - AI Tech Talks, Demo & Social: AI Product Management and Animation. Hosted by David Cunningham @ New York AI Users. In Person RSVP.
Deep Dive Series: Getting into Fundamentals
Jan.11 13:30-14:30 EST | FDS Talk: “Bridging gaps between metrics and goals in modern machine learning ecosystems,” Serena Wang (UC Berkeley). Hosted by Yale Institute for Foundations of Data Science, Zoom RSVP.
Jan.15 15:30-16:30 EST | FDS Talk: “Physics-informed machine learning: Theory, Algorithms, and Applications,” Sifan Wang (UPenn). Hosted by Yale Institute for Foundations of Data Science, Zoom RSVP.
Jan.17 13:00-14:00 EDT | Emotion AI: Separating Facts from Fiction. Hosted by The Institute for Experiential AI, Northeastern University Zoom RSVP.
AI Conferences Callout: Prepare early!
Apr.18-19 2024 | AI in Finance Summit NYC. Hosted by Re•Work, Conference Details.
TOP Events Details
Application Series: Leveraging AI
Lewis Johnson: How to Harness Generative AI to Transform Human Learning [RSVP]
Time & Location: Jan.10 19:00-20:30 EST @ Zoom
Featuring Speaker:
Lewis Johnson, Ph.D. Dr. Johnson holds a B.A. in linguistics from Princeton University and a Ph.D. in computer science from Yale University. He is an internationally recognized leader in innovation for education and training. In 2012 he was keynote speaker at the International Symposium on Automated Detection of Errors in Pronunciation Training in Stockholm. In 2013 he was keynote speaker at the IASTED Technology Enhanced Learning Conference and was co-chair of the Industry and Innovation Track of the AIED 2013 conference. In 2014 he was keynote speaker at the International Conference on Intelligent Tutoring Systems, and was Distinguished Lecturer at the National Science Foundation. In 2015 he was keynote speaker at the ACT Insight Analytics and Emerging Technologies Symposium. When not engaged in developing disruptive learning products Lewis and his wife Kim produce Kona coffee in Hawaii.
This month we’re joined by Lewis Johnson, Ph.D., a leading authority on innovative education methods utilizing the transformative potential of AI. His company, Alelo, builds AI tools to meet the various challenges of education. Q&A follows!
David Cunningham @ NYC AI Users - AI Tech Talks, Demo & Social: AI Product Management and Animation [RSVP]
Time & Location: Jan.16 19:30-22:30 EST @ Merlyn Mind, 8 W 40th St 20th Floor · NYC
Featuring Speaker:
Nishtha Arora, Director of Product at a leading Pharma SaaS company, boasting over 12 years of extensive leadership across renowned industry players like Walmart (E-commerce), top 10 players in Travel, and Softbank & Google-backed Edtechs
Carlos Cisneros, Carlos Cisneros is an engineer based out of Brooklyn, NY. His focus currently is experimenting with generative AI tools by creating surreal animation and music videos, leveraging Stable Diffusion XL, RunwayML, and Warpfusion.
AI technology like OpenAI's ChatGPT, Google's Bard, Midjourney, Dall-E, and many others will play an increasingly large role in our day-to-day lives. Product Managers will have a whole new domain and tools to consider in achieving their goals, and animators will have new, exciting capabilities to draw on. To this end, New York AI Users will host at the Bryant Park-adjacent, beautiful startup HQ of EdTech & AI firm Merlyn Mind! Merlyn will also provide free pizza and drinks! From the creators and organizers of New York Tech & Beer®, New York AI Users is partnering with Merlyn Mind to create high-quality AI events that both educate and connect AI enthusiasts in the New York area who use or want to use AI to meet their creative, entrepreneurial, and technical aspirations. No technical background is required, only an interest in learning about these tools of the future.
Deep Dive Series: Getting into Fundamentals
[ML] FDS Talk: “Bridging gaps between metrics and goals in modern machine learning ecosystems,” Serena Wang (UC Berkeley) [RSVP]
Time & Location: Jan.11 13:30-14:30 EST@Zoom
Featuring Speaker:
Serena Wang
Speaker Bio: I am a final-year PhD student in Computer Science at University of California, Berkeley, advised by Michael I. Jordan. I am generously supported by the NSF Graduate Research Fellowship and the Apple Scholars in AI/ML PhD fellowship. I have also concurrently worked at Google Research at 20% time for the last six years, where I am part of the Discrete Algorithms Group with Ravi Kumar and previously worked with Maya Gupta.
The increasing sophistication and proliferation of machine learning (ML) across public and private sectors has been met with both excitement and apprehension – how do we study societal impacts in this new frontier? Key to understanding the societal impacts of ML is understanding the development and deployment of such systems, which is driven by numerical metrics such as accuracies, click rates, revenue, etc. Unfortunately, these metrics often don’t capture all developer goals or eventual societal impacts, which makes auditing and improving these systems difficult for both engineers and policymakers. In this talk, I will discuss two main approaches to bridging gaps between metrics and goals. First, I will discuss implementation gaps between theory and practice in Fair ML, using robust optimization approaches to handle distributional uncertainty. Second, moving beyond standard “fairness” paradigms, I will discuss recent work on understanding how metrics fit into an ecosystem of stakeholders. Specifically, I will show how causal metrics can improve incentives induced by ranking and accountability systems.
[ML] FDS Talk: “Physics-informed machine learning: Theory, Algorithms, and Applications,” Sifan Wang (UPenn). [RSVP]
Time & Location: Jan.12 10:00-11:00 EST @ Zoom
Featuring Speaker:
Sifan Wang
Speaker Bio: I am Ph.D student in Applied Math and Computational Science at the University of Pennsylvania. My research interests lie in the emerging area of physics-informed machine learning with an emphasis on physics-informed neural networks and DeepONets.
Abstract: The remarkable potential of deep learning in areas from computer vision to natural language processing has now found profound implications in modeling and simulating physical systems. Central to these advancements is the emerging field of physics-informed machine learning (PIML), a fusion of physical principles with machine learning frameworks. Our study delves into the inherent challenges and limitations of PIML, particularly in the physics-informed neural networks (PINNs) and deep operator networks (DeepONets). Firstly, we investigate the gradient flow of PINNs, identifying a training failure stemming from unbalanced back-propagated gradients. This insight motivates us to generalize the Neural Tangent Kernel (NTK) theory to PINNs. With this tool, we theoretically reveal that the training of PINNs suffer from spectral bias, causality violation and discrepancy in convergence rate of loss term. To address these critical issues, we propose several simple yet effective training algorithms and network architectures, and validate them across a wide range of representative benchmarks in computational physics.
Moreover, we highlight the data-intensive demands of training neural operators and the potential inconsistency of their predictions with the underlying physics. To resolve these challenges, we propose physics-informed DeepONet, introducing a simple and effective regularization mechanism for biasing the outputs of DeepONet models towards ensuring physical consistency. Based on that, we propose a autoreressive training algorithm for performing long-time integration of evolution equations in a self-supervised manner. Furthermore, we leverage the proposed framework to build fast and differentiable surrogates for rapidly solving PDE-constrained optimization problems, even in the absence of any paired input-output training data. In summary, we provide in-depth exploration into theory, algorithms and applications aspects of physics-informed machine learning, providing new insights into developing scientific machine learning with better robustness and accuracy guarantees, as needed for many critical applications in computational science and engineering.
[NeuroAI] Emotion AI: Separating Facts from Fiction [RSVP]
Time & Location: Jan.17 13:00-14:00 EST@Zoom
Featuring Speaker:
Lisa Feldman Barrett, University Distinguished Professor of Psychology at Northeastern University
Lisa Feldman Barrett will kick off our Spring 2024 Seminar Series on Wednesday, Jan. 17, 2024 at Northeastern University’s Curry Student Center. In her Expeditions in Experiential AI Seminar “Emotion AI: Separating Facts from Fiction,” Lisa will discuss her revolutionary research in psychology and neuroscience, as well as a radically new scientific understanding of what emotions are, how they work, and implications for emotion AI efforts. Register for free to join us in-person or online!
This address will describe recent scientific discoveries about the nature of emotion with implications for emotion AI efforts. Lisa will explore a radically new scientific understanding of what emotions are and how they work, supported by evidence from psychology, neuroscience, and evolutionary biology: Emotions are not built into your brain from birth, but are built by your brain, instance by instance, as needed.
AI Conferences
AI in Finance Summit NYC 2024 [RSVP]
Time & Location: Apr.18-Apr.19 @ etc. 360 Madison, 360 Madison Ave, New York, NY
Featuring Speakers (Full List here):
Diana Meditz, Director of Advanced Digital Solutions AI/ML at BNY Mellon
Henry Ehrenberg, Co-founder, Snorkel AI
Harry Mendell, Data Architect and Artificial Intelligence Co-Chair, Federal Reserve Bank of New York
…
Get the latest cutting-edge use cases from people leading AI initiatives from the most innovative companies in BFSI.
Learn how companies from other sectors are:Using the most effective ML Models to combat fraud
Advancing conversational AI for chatbots with NLP
Embracing ethical and transparent AI practices to protect the privacy of their customers
... And much more!