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<\/h4>\n<\/h4>\nArt and Generative AI<\/p>\n
I see many students viewing artificial intelligence as humanlike simply because it can write essays, do complex math or answer questions. AI can mimic human behavior but lacks meaningful engagement with the world. This disconnect inspired the course and was shaped by the ideas of 20th-century German philosopher\u00a0Martin Heidegger<\/a>. His work highlights how we are deeply connected and present in the world. We find meaning through action, care and relationships. Human creativity and mastery come from this intuitive connection with the world. Modern AI, by contrast, simulates intelligence by processing symbols and patterns without understanding or care.<\/p>\n In this course, we reject the illusion that machines fully master everything and put student expression first. In doing so, we value uncertainty, mistakes and imperfection as essential to the creative process.<\/p>\n This vision expands beyond the classroom. In the 2025-26 academic year, the course will include a new community-based learning collaboration with Atlanta\u2019s art communities. Local artists will co-teach with me to integrate artistic practice and AI.<\/p>\n The course builds on my 2018 class,\u00a0Art and Geometry<\/a>, which I co-taught with local artists. The course explored Picasso\u2019s cubism, which depicted reality as fractured from multiple perspectives; it also looked at Einstein\u2019s relativity, the idea that time and space are not absolute and distinct but part of the same fabric.<\/p>\n We begin with exploring the first mathematical model of a neuron, the\u00a0perceptron<\/a>. Then, we study the\u00a0Hopfield network<\/a>, which mimics how our brain can remember a song from just listening to a few notes by filling in the rest. Next, we look at\u00a0Hinton\u2019s Boltzmann Machine<\/a>, a generative model that can also imagine and create new, similar songs. Finally, we study today\u2019s\u00a0deep neural networks<\/a>\u00a0and\u00a0transformers<\/a>, AI models that mimic how the brain learns to recognize images, speech or text. Transformers are especially well suited for understanding sentences and conversations, and they power technologies such as ChatGPT.<\/p>\n In addition to AI, we integrate artistic practice into the coursework. This approach broadens students\u2019 perspectives on science and engineering through the lens of an artist. The first offering of the course in spring 2025 was co-taught with\u00a0Mark Leibert<\/a>, an artist and professor of the practice at Georgia Tech. His expertise is in\u00a0art, AI and digital technologies<\/a>. He taught students fundamentals of various artistic media, including charcoal drawing and oil painting. Students used these principles to create art using AI ethically and creatively. They critically examined the source of training data and ensured that their work respects authorship and originality.<\/p>\n Students also learn to record brain activity using electroencephalography \u2013 EEG \u2013 headsets. Through AI models, they then learn to transform neural signals into music, images and storytelling. This work inspired performances where dancers improvised in response to AI-generated music.<\/p>\nWhat does the course explore?<\/strong><\/h4>\n