AI Innovation Institute

Join us for the New York State Innovation Summit on October 28-29, 2024 in Syracuse, NY. This multi-day is event for NYS organizations that want to showcase and discover new and emerging technologies that support innovation and drive business growth. The event serves as an opportunity to foster collaboration; introduce industry to experts that can assist growth, strengthen our statewide innovation ecosystem and showcase promising early stage companies. Whether you're a startup, an economic developer, or an established manufacturer, the NYS Innovation Summit is for you. The 2024 New York State Innovation Summit will showcase companies and researchers at the forefront of emerging technologies and new advancements in production capabilities. This event celebrates New York State leadership in technology-led economic growth with experts in biotechnology, new materials, energy innovation, and artificial intelligence that will explore current technology convergence opportunities, ways to accelerate commercialization, and issues of manufacturing sustainability.

AI Institute Seminar Title: A Geometric Understanding of Deep Learning Abstract: This work introduces an optimal transportation (OT) view of generative adversarial networks (GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold distribution principle: the distribution of a class of data is close to a low-dimensional manifold. GANs mainly accomplish two tasks: manifold learning and probability distribution transformation. The latter can be carried out using the classical OT method. From the OT perspective, the generator computes the OT map, while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution; both can be reduced to a convex geometric optimization process. Furthermore, OT theory discovers the intrinsic collaborative--instead of competitive--relation between the generator and the discriminator, and the fundamental reason for mode collapse. We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT map for probability distribution transformation. This AE-OT model improves the theoretical rigor and transparency, as well as the computational stability and efficiency; in particular, it eliminates the mode collapse. The experimental results validate our hypothesis, and demonstrate the advantages of our proposed model.

Zoom Link: https://github.com/giorgianb/spdhackspring2021/blob/main/bit.ly/spdhack2021

ΣΦΔ Hack Spring 2021 is ΣΦΔ's first annual machine learning hackathon. ΣΦΔ Hack Spring 2021 aims to introduce Stony Brook students to the rich and challenging field of machine learning, and develop the skills necessary to build sophisticated machine learning models on their own.
 
More info here: https://github.com/giorgianb/spdhackspring2021/blob/main/README.md

Abstract: This dissertation addresses the methodological disconnect between Natural Language Processing (NLP) and human-centric analysis by shifting the unit of analysis from document to human behavior in two broad respects: (i) time-ordering: modeling documents as sequential person-indexed behavioral observations, and (ii) person-level semantics: evaluation and explainability of models by their latent structure of psychological constructs rather than just its predictive accuracy against narrow proxy measures. First, we consider the most basic implication of language as a person's behaviors when measuring their psychological constructs: relationship between language sample size and model's predictive performance. We empirically show that the state-of-the-art transformers are often over-parameterized for typical NLP dataset sizes and can be reduced in dimensionality without performance loss. Establishing the author as the unit of analysis naturally allows us to treat their behavior as a time-ordered sequence. Second, we introduce a longitudinal evaluation framework that establishes ecologically valid evaluation settings, namely, cross-sectional and prospective generalization, and separates error measurement of the model into within-person dynamics and between-person differences. We demonstrate that traditional NLP evaluations based on random document splits can yield reversed conclusions under ecologically valid generalization settings. To address this, we develop models that capture the trajectory of mental states (e.g., mood shifts) rather than static traits. Third, moving into person-level semantics, we evaluate the latent structure of large language models using a novel machine behavior analytic framework. We find that while GPT-4 achieves high predictive correlation with self-reports, its latent symptoms structure diverges from clinical understanding. Finally, we propose a method for modeling multidimensional behaviors, embedding concurrent behavioral signals alongside language to predict future states. Taken together, this work suggests that operationalizing language as behavior advances NLP methods into a rigorous instrument for valid psychological inquiry.

Speaker: Adithya Ganesan

Location: Join Zoom Meeting (ID: 99021939129, Passcode: 569493)

The infrastructure decisions being made around AI today will shape competitive advantage, cost, control, and institutional trust for years to come.

Organizations will invest in AI either way. The strategic question is whether they remain dependent on systems they cannot fully inspect or influence, or help shape shared infrastructure that they can adapt, govern, and build upon.

We would like to invite you to a live executive briefing from the AI Alliance. The discussion will focus on what leaders should understand now:

• The state of the movement: how 205 organizations across 29 countries are collaborating on more than 30 open projects.
• Project Tapestry: our global initiative to build frontier-capable, open-sourced AI through shared development, while allowing participating institutions to retain control over their data, resources, and strategic priorities.
• The business and policy landscape: how cost, sovereignty, trust, and access are reshaping AI strategy.
• Paths to participation: how organizations can contribute through the AI Alliance Open R&D Community and the Open Source AI Innovation Association.

Register here.

Objectives:
1. Explain the clinical radiology workflow, and highlight how AI is currently in use to impact each step
2. Describe how radiologists interact with the currently available tools, highlighting both positive andnegative examples
3. Offer a brief description of how these tools are approved, validated, and reimbursed
4. Explore the utility of cutting edge AI techniques in diagnostic radiology

Speaker:
Dr. David Payne, MD Neuroradiologist and Assistant Professor, Rush University Medical Centre

Remote Access:
Zoom: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 95617197636
Passcode: 924293

AI + Music Seminar - Details to come.