AI and Me

Rapid advancements in machine learning and generative artificial intelligence tools, such as ChatGPT and DALL-E 2, have spurred lively discussions about what the future of AI holds for humankind.
Considering the seemingly limitless potential of AI, how do the foremost AI researchers strike a balance between comprehending AI’s vast and unpredictable nature while also understanding their own role in the equation?

Stephanie Dinkins is a transmedia artist who uses AI and emerging technologies to create experiences that spark dialog about race, gender, aging and our future histories.

As a computational mathematician currently developing a new set of learning algorithms to improve machine learning optimization, Neha Wadia often ponders the role of academic researchers in this space.

AI researcher at New York University Kyunghyun Cho advocates for a shift in discourse, moving from sensationalized existential risks to a critical examination of both the immediate benefits and harms associated with AI.

Join them as they sit down with John Tracey, program director of Science, Society and Culture at the Simons Foundation, for a conversation about their personal connections to AI and their thoughts on the broader societal implications of these technologies.

Dinkins is a transmedia artist whose work in AI and other mediums uses emerging technologies and social collaboration to work toward technological ecosystems based on care and social equity. Dinkins’ experiences with and explorations of artificial intelligence have led to a deep interest in how algorithmic systems impact communities of color in particular and all of our futures more generally. Dinkins teaches at Stony Brook University, holding the Kusama Endowed Chair in Art. Dinkins earned an MFA from the Maryland Institute College of Art and is an alumna of the Whitney Independent Studies Program. She exhibits and publicly advocates for inclusive AI internationally at a broad spectrum of community, private and institutional venues.

Wadia joined the Center for Computational Mathematics as a Flatiron Research Fellow in August 2022. She works on problems in optimization for machine learning but has many other interests, including statistical mechanics, high-dimensional statistics and neuroscience. Wadia earned a Ph.D. in Biophysics from the University of California, Berkeley, in May 2022 and holds degrees in physics from the Perimeter Institute for Theoretical Physics and Amherst College.