[NYC AI Event Picks] Action Packed February!
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
NYC IRL: Learn & Meet Fellow Folks working on AI!
Feb.1 18:00-20:00 EST | NYC 🗽 Break into AI Panel + Happy Hour. Hosted by The AI Furnace. Hybrid RSVP.
Feb.6 17:30-21:00 EST | ML/AI Conversation: GraphNN and Reasoning Improvement for LLM. Hosted by New York AI/ML Conversations. Hybrid RSVP.
Feb.13 18:00-21:00 EST | AI Tinkerers NYC Feb 2024 Meetup. Hosted by AI Tinkerers. In Person RSVP.
Feb.13 19:30-22:30 EST | NYC AI Users - AI Tech Talks, Demo & Social: AI in Finance & LLMs 101. Hosted by David Cunningham @ New York AI Users. In Person RSVP.
Application Series: Leveraging AI to solve real world issues!
Feb.07 12:00-12:30 EST | Designing with generative AI to augment human creativity. Hosted by MIT. Zoom RSVP.
Feb.14 13:00-14:00 EST | From Ethics Oversight to Integration: Reshaping the Role of IRBs for AI Research. Hosted by The Institute for Experiential AI, Northeastern University Hybrid RSVP.
Feb.15 12:00-13:00 EST | Learning to deliver: How AI and machine learning will transform the logistics industry. Hosted by MIT. Zoom RSVP.
Feb.22 12:00-13:00 EST | The Technology, Policy and Economics of Creating Safer Roads Webinar. Hosted by The Marconi Society. Zoom RSVP.
Deep Dive: Neuroscience, Engineering and AI
Feb.12 16:00-17:00 EST | Fine-Grained Extensions of the Low-Degree Testing Framework. Hosted by The Department of Statistics and Data Science, Yale University. Zoom RSVP.
Feb.14 09:00-11:00 EST | [NeuroAI] Unifying the mechanisms of hippocampal episodic memory and prefrontal working memory. Hosted by University College London. Zoom RSVP.
Feb.14 18:30-19:45 EST | Stavros Niarchos Foundation Brain Insight Lecture. Hosted by The Zuckerman Institute, Columbia University. Zoom RSVP.
TOP Events Details
NYC IRL: Learn & Meet Fellow AI Enthusiasts!
NYC 🗽 Break into AI Panel + Happy Hour
Time: Feb.1 18:00-20:00 EST
Hybrid: RSVP
Featuring Speaker:
Edwin Jain, Founder of Monoid (ex-Goldbelly, AI/ML research background)
Saumya Rawat, Co-Founder of Pharmesol (ex-Peloton, MIT alumni)
Neel Shah, Co-Founder of EZ Newswire (Wharton + LSE grad)
The AI Furnace is NYC's largest and most active AI community. It was started by AI founders, Angela Mascarenas and Hamza Zaveri, who were based in NYC and London and started the org to support fellow AI founders and operators building in local cities outside of Cerebral Valley (San Francisco). It has since grown to a global community of 10,000 AI founders, operators and researchers and has since opened up in London and Boston.
ML/AI Conversation: GraphNN and Reasoning Improvement for LLM
Time: Feb.6 17:30-21:00 EST
Hybrid: RSVP
Featuring Speaker
Max Eremeev, AI Research Engineer (Elemental Cognition) - "Curriculum training to improve reasoning of LLMs".
Anton Tsitsulin, Research Scientist (Google) - "Graph Neural Networks in TensorFlow".
At Elemental Cognition we’re trying to tackle complex issues for high-stakes domains, e.g., pharma or investment management. And while everyone is fooled by the fluency of LLMs, they are not applicable a high risk tasks because of hallucinations or degenerate outputs. At EC, we tackle this problem by employing curriculum training — we train hundreds of adapters on top of 7B models, progressively increasing the complexity of use cases. This talk is dedicated to learn more about good practices of curriculum learning and useful training/generation regularization techniques for resolving typical degeneracies.
Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are an exciting way to use graph structured data inside neural network models that have recently exploded in popularity. However, implementing GNNs and running GNNs on large (and complex) datasets still raises a number of challenges for machine learning platforms. The talk will be based on TF-GNN, a library for working with graph structured data in TensorFlow.
AI Tinkerers NYC Feb 2024 Meetup
Time: Feb.13 18:00-21:00 EST
In Person: RVSP
AI Tinkerers is a meetup designed exclusively for practitioners who possess technical, machine learning, and entrepreneurial backgrounds and are actively building and working with foundation models, such as large language models (LLMs) and generative AI. If you’re deeply passionate about creating LLM-enabled applications, have hands-on experience in building such systems, and are eager to connect with like-minded individuals who share your level of commitment, then this group is the perfect fit for you. With AI Tinkerers meetups taking place in multiple cities, we cater to a dedicated community of practitioners.
Who is this for?
We’re not “AI Enthusiasts”, we are AI Tinkerers. The core essence of AI Tinkerers lies in active collaboration surrounding early-stage discovery and innovation, which requires a high degree of experimentation, vulnerability, openness to sharing challenges and learnings, and collaboration among individuals with a shared level of expertise. This unique environment allows us to push the boundaries of what’s possible with AI and LLMs while maintaining a strong sense of camaraderie and mutual support.
NYC AI Users - AI Tech Talks, Demo & Social: AI in Finance & LLMs 101
Time: Feb.13 19:30-22:30 EST
In Person: RSVP
Featuring Speaker:
Sabri Monaf, Senior ML Engineer at Kiva.
Logan Weaver, founder & CEO of Surmount AI, a Techstars alumni, and WBJ 25 Under 25.
Sabri who will explore the current landscape of Large Language Models (LLMs), teaching the basics of how these models function, and examining both open-source and closed-source offerings. We'll then dive into the tools and techniques that empower you to harness the full potential of these models, opening a world of possibilities in AI-powered applications. Sabri is a software and machine learning engineer with over decade in experience and a Master's in AI. He primarily serves as a Senior ML Engineer at Kiva, working to expand financial services to underserved communities, and also leads tech initiatives at AI Dojo, focusing on advancing the AI landscape in Iraq.
Logan will share his thoughts on AI/ML in finance and breaks down what the future of AI in finance could look like.
Application Series: Leveraging AI to solve real world issues!
Designing with generative AI to augment human creativity
Time: Feb.07 12:00-12:30 EST
Zoom: RSVP
Featuring Speaker:
Steven Rick of the MIT Center for Collective Intelligence
Recent advances in computing enable machines to produce humanlike textual and visual content on a never-before-seen scale, raising the question of whether technology will replace human creativity. Addressing this concern, Steven Rick of the MIT Center for Collective Intelligence will discuss observations from research studies and real-world deployments through the lens of two systems: DesignAID and Supermind Ideator. Rick will show why these new AI tools are insufficient alone and suggest that we design to augment human creativity through collectively intelligent human-machine teams.
From Ethics Oversight to Integration: Reshaping the Role of IRBs for AI Research
Time: Feb.14 13:00-14:00 EST
Hybrid: RSVP
Featuring Speaker:
Cansu Canca, Director of Responsible AI Practice at the Institute for Experiential AI and Research Associate Professor in Philosophy at Northeastern University
With the developments in artificial intelligence (AI), the need for an ethical framework for AI research becomes pressing. In recognition of this need, a growing opinion in academia, research industry, and policy discussions has been favoring a system that is modeled after the research ethics framework in bioethics. This framework is one of ethics oversight and compliance, where institutional review boards (IRBs) act as authorities evaluating the ethical aspects of research protocols guided by a set of principles. Cansu argues that this IRB system’s existing challenges only get worse as it expands to evaluate AI systems. IRB system is ill-equipped to handle AI-specific moral questions and keep with the pace of AI research. Looking at the type of ethical issues that AI technology poses and on whom the risk of harm fall, the needed ethics framework goes well beyond research and encompasses the whole innovation lifecycle from research, development, and design to monitoring and updating processes. And the organizational structure to implement such a framework goes beyond the expertise, capacity, and scope of IRBs. Instead of acting as ethics authorities evaluating AI systems, she proposes a function for IRBs as requiring an ethics integration and risk mitigation plan for research protocols and overseeing the robustness and the competence of the proposed plan and mechanism for the AI innovation lifecycle.
Learning to deliver: How AI and machine learning will transform the logistics industry
Time: Feb.15 12:00-13:00 EST
Zoom: RSVP
Featuring Speaker:
Matthias Winkenbach, the Director of the MIT Megacity Logistics Lab and a Principal Research Scientist at the MIT Center for Transportation & Logistics
Machine learning and artificial intelligence have the potential to enable significant efficiency gains and new levels of customer service across the supply chain and logistics industry. MIT scientist Matthias Winkenbach will touch upon several machine learning and artificial intelligence applications that his team is working on in collaboration with major industry partners. He will share his most recent work on applying state-of-the-art generative AI models to improve route planning based on driver experience. He will further share his outlook on the potential of the recent advancements in large language models, such as ChatGPT, to democratize the use of advanced analytics across all levels of a supply chain organization.
The Technology, Policy and Economics of Creating Safer Roads Webinar
Time: Feb.22 12:00-13:00 EST
Zoom: RSVP
Featuring Speaker:
Hari Balakrishnan, 2023 Marconi Fellow, Co-founder and CTO of Cambridge Mobile Telematics
Steve Kiefer, Founder of The Kiefer Foundation, former General Motors executive
Dr. Kit Delgado, MD, MS, Associate Professor of Emergency Medicine, University of Pennsylvania
Amanda Mezerewski, VP Product – Auto Travelers
Precious Nduli, Director of Marketing, Discovery Insure
Dr. Todd Shurn, Associate Professor of Computer Science, Howard University
Vehicles are safer than ever. Yet in 2021, over 42,000 people were killed on American roads, the highest number in 16 years. Pedestrian fatalities surged to 40-year highs. Cyclist deaths reached levels not seen since 1990.
What’s behind this deadly trend and how can we stop it? The answer lies in a unique blend of technology, policy, and economics that drive consumer behavior.
Our expert panel will discuss and answer your questions about:
The technological advancements in sensor processing and artificial intelligence that have enabled the largest auto insurers to invest billions in safe driving incentives.
Economic trends driving consumers to search for savings.
How rewards and other incentives improve driving behavior and save lives.
How policy-driven solutions, like hands-free legislation, have impacted driving risk and how states and cities have implemented them.
Deep Dive: Neuroscience, Engineering and AI
Feb.12 16:00-17:00 EST | Fine-Grained Extensions of the Low-Degree Testing Framework. Hosted by The Department of Statistics and Data Science, Yale University. Zoom RSVP.
Feb.14 09:00-11:00 EST | [NeuroAI] Unifying the mechanisms of hippocampal episodic memory and prefrontal working memory. Hosted by University College London. Zoom RSVP.
Feb.14 18:30-19:45 EST | Stavros Niarchos Foundation Brain Insight Lecture. Hosted by The Zuckerman Institute, Columbia University. Zoom RSVP.
Fine-Grained Extensions of the Low-Degree Testing Framework
Time: Feb.12 16:00-17:00 EST
Hybrid: RSVP
Featuring Speaker:
Alex Wein, Assistant Professor of Mathematics at UC Davis
The low-degree polynomial framework has emerged as a versatile tool for probing the computational complexity of statistical problems by studying the power and limitations of a restricted class of algorithms: low-degree polynomials. Focusing on the setting of hypothesis testing, I will discuss some extensions of this method that allow us to tackle finer-grained questions than the standard approach.
First, for the task of detecting a planted clique in a random graph, we ask not merely when this can be done in polynomial time O(n^c), but seek the optimal exponent c as a function of the clique size. To this end, we consider algorithms that make non-adaptive edge queries and then apply a low-degree test, and we determine the number of queries required. This is joint work with Jay Mardia and Kabir Verchand.
Second, in the spiked Wigner model with any iid spike prior, we seek the precise optimal tradeoff curve between type I and type II error rates. Conditional on an appropriate strengthening of the “low-degree conjecture,” we show that tests based on the spectrum achieve the best possible tradeoff curve among poly-time algorithms, while exponential-time non-spectral tests can do better. This is joint work with Ankur Moitra.
[NeuroAI] Unifying the mechanisms of hippocampal episodic memory and prefrontal working memory
Time: Feb.14 09:00-11:00 EST
Zoom: RSVP
Featuring Speaker:
James Whittington - Stanford University / University of Oxford
Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as structured, and controllable, neural subspaces (termed activity slots). By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM
[NeuroAI] Stavros Niarchos Foundation Brain Insight Lecture
Time: Feb.14 18:30-19:45 EST
Hybrid: RSVP
Featuring Speaker:
Dani Dumitriu, MD, PhD, Assistant Professor of Pediatrics (in Psychiatry) at Columbia University Irving Medical Center
Dima Amso, PhD, Professor of Psychology at Columbia University
Katie Insel, PhD, Postdoctoral Research Fellow at Columbia University’s Zuckerman Institute
The first few years of our lives are a fascinating and crucial time, filled with impressive cognitive and social development. We start to figure out not only who we are but also the relationships we have with the people closest to us. As we progress through infancy and childhood, how do the connections we form with caregivers nurture us as we navigate an increasingly complex and ever-changing environment? In this event, three experts studying early life development will bring together perspectives from psychology, neuroscience, and pediatrics to discuss how children grow up in a dynamic world.