Decoding the Invisible: Latent Representation and the Future of Human-AI Interaction
Modern AI does not understand pixels or words. It understands latent structure. As these invisible representations become the language through which humans and machines communicate, they will fundamentally redefine how we create, discover, and collaborate. This lecture reveals the mathematics behind these hidden spaces and explores why they may become the most important interface between human intelligence and artificial intelligence.
This lecture explores the evolution of latent representations from Variational Autoencoders, which first learned meaningful probabilistic embeddings, to Generative Adversarial Networks, which demonstrated photorealistic generation while exposing challenges in stability and control, and finally to diffusion models and Stable Diffusion, which combine exceptional image quality with unprecedented controllability and robustness.
Beyond the algorithms, the talk examines how latent representations are transforming human-AI interaction. By enabling AI systems to reason, generate, and collaborate through shared abstract representations, they are reshaping creativity, scientific discovery, healthcare, education, and engineering. Understanding these hidden spaces offers not only insight into how modern AI works, but also a glimpse into a future where humans and intelligent machines communicate and co-create in entirely new ways.
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Co-sponsored by: Vishnu S. Pendyala, SJSU
Speaker(s): Dr. Narayan Srinivasa, IEEE Fellow , Dr. Vishnu S. Pendyala
Virtual: https://events.vtools.ieee.org/m/570049