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AI & SOCIETY SEMINAR

AI & SOCIETY SEMINAR

Thursday, January 25, 2024

Please RSVP by Wednesday 12 pm to aifellows@nyas.org for a zoom link or if you plan to attend in-person.

 

Please RSVP by Wednesday 12 pm to aifellows@nyas.org for a zoom link or if you plan to attend in-person.


About Basis, Zenna Tavares, PhD

ChiRho: A Causal Probabilistic Programming Language, Sam Witty, PhD

Bridging Different Understandings of Collaborative Intelligent Systems, Emily Mackevicious, PhD

AI & SOCIETY SEMINAR

Details:

1.

Title: About Basis

Speaker: Zenna Tavares, PhD

Speaker Bio:

Zenna Tavares is a co-founder and director of Basis, Innovation Scholar in Columbia University’s Zuckerman Mind Brain Behavior Institute, and Associate Research Scientist in the Data Science Institute. Tavares’s research aims to understand how humans reason, that is, how they come to derive knowledge from observing and interacting with the world. He constructs computational and statistical tools that help advance his work on causal reasoning, probabilistic programming, and other areas.

Abstract:

Basis Research Institute is a 501(c)(3) nonprofit research organization with two mutually reinforcing goals: to understand and build intelligence, and to advance society’s ability to solve intractable problems. Basis is building technology that can reason about and operate in environments of the kind of boundless complexity and detail that reality has to offer. To do this, we will focus on how to represent and discover models of phenomena in the world at unprecedented fidelity and scale, incorporating available knowledge of all kinds, be it large or small amounts of data, interactions and experiments, or the wealth of tacit knowledge accumulated by human experts. We aim to publish technical results regularly at leading venues like ICML, NeurIPS, and PLDI but our primary research medium will be a digital lingua franca of artificial intelligence: a unified, growing body of open-source software that serves as a bidirectional interface with the rest of the world. One example of this is ChiRho, an experimental language for causal reasoning. Our team’s prior research work has also realized parts of this ambitious vision, developing languages and methods for causal reasoning (Omega and SBI), probabilistic machine learning (Pyro and NumPyro), and computational neuroscience and cognitive science (successor representation learning and Autumn). https://www.basis.ai/

AI & SOCIETY SEMINAR

2.

Title: ChiRho: A Causal Probabilistic Programming Language

Speaker: Sam Witty, PhD

Speaker Bio:

Sam Wittyis a research scientist at Basis and an affiliate researcher at the Broad Institute of MIT and Harvard. He was previously a visiting researcher in MIT’s Brain and Cognitive Sciences Department, and has a PhD in computer science from UMass Amherst. His research goal is to develop AI and ML methods that help scientists discover more and that help policymakers make better decisions.

Abstract:

Despite remarkable progress over the last two decades in reducing causal inference to statistical practice, the "causal revolution" proclaimed by pioneering computer science and statistics researchers remains incomplete, with a fragmented technical literature that is still inaccessible to non-experts and isolated from the cutting-edge computational methods and software tools being developed within mainstream machine learning research. This talk will introduceChiRho(https://github.com/BasisResearch/chirho), a new causal probabilistic programming language embedded in Python. ChiRho bridges the gap between the capabilities of modern probabilistic programming systems, such as Pyro, and the needs of policymakers, scientists, and AI researchers, who often want to use models to answer their questions about cause-and-effect relationships. In this talk I provide an overview of ChiRho's capabilities, showing how it unifies a large set of previously disparate methods scattered throughout the causal inference literature. I will also illustrate ChiRho's design with representative example applications from econometrics, single-cell biology, and epidemiology.

AI & SOCIETY SEMINAR

3.

Title: Bridging Different Understandings of Collaborative Intelligent Systems

Speaker: Emily Mackevicious, PhD

Speaker Bio:

Dr. Emily Mackeviciusinvestigates how intelligent behaviors arise in distributed and recurrent systems, with a focus on birds. Her researchbridges multiple levels of abstraction, from neural mechanisms, to cognitive functions, to collaborative group behaviors. She is affiliated withColumbia’sZuckerman InstitutethroughAronov Laband theCenter for Theoretical Neuroscience, and she co-founded a new research institute,Basis, which develops open-source AI code applied to real-world problems.

Abstract:

Every natural intelligent system faces the fundamental challenge of managing collaboration, since intelligent systems are built and grow from largely independent parts, from cells to organisms to ecosystems. Acollaborative intelligent systemoccurs when groups of people or animals share information, and collaborate on mutually compatible goals. Collaborative intelligent systems are studied from many perspectives, including cognitive science, neuroscience, statistics, and dynamical systems. I’ll describe strategies for bridging and integrating different understandings, focusing on the foraging behavior of groups of birds and insects.

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