Science & Technology - Lectures

Building quantum technology requires a lot of people with different backgrounds (physicists, electrical engineers, computer scientists, software engineers, chemists, …) and it can be overwhelming to consider a career in quantum science and information. By watching this series of panels hosted in partnership with the Center for Quantum Networks Engineering Workforce Development, viewers will learn from students and researchers in the field what it is like to work in quantum science and get advice on navigating the various pathways. This and future panels in this series will be dedicated to share information on careers in the field of quantum science and information at all levels. For more information on everything quantum at Yale, visit quantuminstitute.yale.edu

Our decisions define us, however, we rarely reflect on the process by which we make them. Science has taught us that this path is full of biases and idiosyncrasies. In this talk, acclaimed neuroscientist Mariano Sigman will explicate this nebulous process, addressing why sometimes we blindly trust our decisions and other times we doubt, even when the evidence is overwhelming. Our speaker will lay bare the logic and hidden algorithms that often make hunches and intuitions so effective and why we tend to struggle with uncertainty.

The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems. It is then tempting to imagine the path towards developing an autonomous AI scientist also responsible for designing experiments and hypothesis generation. Interestingly, this framing brings a new perspective on the role of causality and agency that incorporates recent advances in machine learning and artificial intelligence.

The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems. It is then tempting to imagine the path towards developing an autonomous AI scientist also responsible for designing experiments and hypothesis generation. Interestingly, this framing brings a new perspective on the role of causality and agency that incorporates recent advances in machine learning and artificial intelligence. Moderated Conversation with Zenna Tavares, Zuckerman Institute, Data Science Institute, Columbia University and Co-Founder of Basis

Yale University’s Wu Tsai Institute and the Schmidt Program on Artificial Intelligence, Emerging Technologies, and National Power co-host the talk, “The Alignment Problem: Machine Learning and Human Values,” by Brian Christian, an award-winning author and Science Communicator in Residence at the Simons Institute for the Theory of Computing at University of California – Berkeley. Christian is recognized as a leading authority on artificial intelligence and the ethical challenges associated with emerging technologies. His latest book, “The Alignment Problem: Machine Learning and Human Values,” is a blend of history and on-the-ground reporting, tracing the explosive growth of machine learning and the wide range of resulting risks, opportunities, and unintended consequences.The book is aLos Angeles TimesFinalist for Best Science & Technology Book of the Year, and Microsoft CEO Satya Nadella has namedit one of the five books that inspired him in 2021. Christian is the author of the acclaime

This is the 14th talk of Yale Quantum Institute series of nontechnical talks aiming to bring a new regard to quantum physics and STEM by having experts cast new light on often-overlooked aspects of scientific work. For The Hapless Dreamers: Digital Art meets Quantum Simulations Stewart Smith is a lucky guy. He gets paid to invent fun things. After working at renowned hybrid studios like Google’s Creative Lab and Data Arts Team, and founded Moar—a technology and strategy consultancy, digital artist and Head of Consumer Augmented Reality at Unity Stewart Smith is helping to craft the future of AR. Join us to experience Stewart Smith’s hapless dream that will bring us to the many detours in his digital art practice takes: maintaining Q.js, a browser-based quantum circuit composer and simulation framework, creating virtual art applications, or exhibiting at the Museum of Modern Art in New York, ZKM in Germany, the SFMOMA in San Francisco. What really makes him happy is partnering with smar

With the rise of quantum computing, it has become evident that our current encryption protocols to send secure communication are not adequate anymore. In this class, hosted by the Yale Quantum Institute for the Jackson School for Global Affairs, Professor Steve Girvin teaches how to send secure communication in a quantum world, using the BB84 protocol for a secure quantum key distribution. The students learned about the protocol and then participated to hands-on small group activities to share secret key securely between each other (from Alice to Bob), and detect possible eavesdroppers (Eve). This class was prepared with the help of Florian Carle (YQI Manager), Jahan Claes (YQI Fellow), Shraddha Singh (Graduate Student), and Kevin Smith (Postdoctoral Associate), and part of the Schmidt Program on Artificial Intelligence, Emerging Technologies, and National Power. Chapters: 00:01 – Introduction by Ted Wittenstein 03:32 – Context of the 2nd quantum revolution 14:12 – Q&A 16:54 – Principl

Brian Cantwell Smith, Reid Hoffman Professor of Artificial Intelligence and the Human, University of Toronto Classical models of inference, such as those based on logic, take inference to be conceptual – i.e., to involve representations formed of terms, predicates, relation symbols, and the like. Conceptual representation of this sort is assumed to reflect the structure of the world: objects of various types, exemplifying properties, standing in relations, grouped together in sets, etc. These paired roughly algebraic assumptions (one epistemic, the other ontological) form the basis of classical logic and traditional AI (GOFAI). In this talk, Professor Smith will argue that the world itself is not conceptual, in the sense of not consisting (at least au fond) of objects, properties, relations, etc. That is, he will argue against the ontological assumption. Rather, he believes that taking the world to consist of the familiar ontological furniture of objects, properties, etc. results from

Brian Cantwell Smith, Reid Hoffman Professor of Artificial Intelligence and the Human, University of Toronto. Moderated conversation with Joseph T. Rouse, Department of Philosophy, Wesleyan University. Classical models of inference, such as those based on logic, take inference to be conceptual – i.e., to involve representations formed of terms, predicates, relation symbols, and the like. Conceptual representation of this sort is assumed to reflect the structure of the world: objects of various types, exemplifying properties, standing in relations, grouped together in sets, etc. These paired roughly algebraic assumptions (one epistemic, the other ontological) form the basis of classical logic and traditional AI (GOFAI). In this talk, Professor Smith will argue that the world itself is not conceptual, in the sense of not consisting (at least au fond) of objects, properties, relations, etc. That is, he will argue against the ontological assumption. Rather, he believes that taking the worl

Professor Jing Tsu, the John M. Schiff Professor of East Asian Languages and Literatures in Yale's Department of Comparative Literature, will discuss the genesis of the project that led to the publication of her recent book, Kingdom of Characters: The Language Revolution That Made China Modern. The Franke Program in Science and the Humanities.