Innovation is at the forefront of classes, research, internships, and the future of Stevens graduates. This concept motivated recent Stevens Physics graduate and the current University of Oxford Ph.D. student Kaitlin Gili to present a series of lectures here at Stevens, exploring Quantum Cognition and Machine Learning.
Throughout April, Gili has given presentations to the Stevens community on Wednesday afternoons during the academic break. Amongst members of the Stevens physics department, computer department, and more, Gili explained different topics of quantum cognition, the science behind those ideas, and their application. Gili has experience as a student at Oxford along with research at Los Alamos National Laboratory, as well as other research institutions around the world.
The Stute spoke with Gili about her presentations. She explained that rather than a particular theory or phenomenon, she hopes students will come away with a sense of curiosity, explaining she wants to, “show students that there are topics like quantum computing and machine learning that are just fundamentally fascinating. We train quantum particles to learn data” and continued to explain how she wants to “give students a glimpse of a different kind of ‘career path’ they could take.” Gili hopes to make an environment “that is less about producing a product or making a financial gain, but is more about asking questions about fundamental things in the universe […] I want to create an environment that ignites that feeling.”
Rather than explain quantum cognition through the traditional classroom styles, Gili chose to use a more innovative and interactive method that allows for an interdisciplinary understanding. She wanted, “to show students and faculty that there are connections to be made across fields: physics, computer science, math, psychology, and neuroscience. We always think of psychology as a very different science than physics, but I see that there are similar structures that exist in each. The mind is fundamentally fascinating, and we don’t understand it enough. Human behavior & the behavior of quantum things have a lot of overlap. Human behavior and classical machine learning behavior have a lot of overlap. It’s all information, and there are mathematical structures in information. It’s why we can design classical AI models to learn natural language like ChatGPT, or it’s why we can design classical AI models to predict human behavior (stocks, Netflix shows, Amazon purchases, Hinge matches, etc.). We can train classical and quantum networks on data because there is so much structure in data.”
With a clearly deep understanding of the topic, Gili also explained her thoughts on the future of machine learning and quantum cognition. She explains that this industry “is going to continue to invent these technologies & algorithms at a rapid rate, and there will be many job opportunities with high pay. These jobs though will focus on the rapid growth of practical use, rather than understanding the technology to the fullest. Academics will be more interested in pushing the understanding, but even in the academic system, there is a lot of bias towards practical usefulness for societal dollars.” Gili went on to explain the need for “folks who understand the technology from the inside, and then we need those folks as consultants for companies and for the government. I worry about the rapid rate of tech being deployed without any kind of regulation or understanding of how it is going to impact society. If technology is in our everyday lives, it influences us heavily. It’s a loop. Artificial intelligence and machine learning algorithms capture human behavior, and once we integrate them into our everyday lives, they will also influence human behavior. Again, how psychology and technology connect.”
Gili’s presentations offered insights on some of the most advanced topics being explored around the world in a way understandable to audiences of all academic backgrounds and knowledge. Offering a great variety of examples and explanations, they prompted conversations that went on long past the end of the presentations. Gili ends with hopes that “rather than having to constantly perform in the learning/education process,” that instead “students give themselves permission to be more curious/creative.”
