AI Videos

Yi Ma - Pursuing the Nature of Intelligence

Professor Yi Ma’s colloquium traced his trans‑Pacific career—from control theory to computer vision, machine learning, and leadership at Microsoft Research Asia—culminating in a deep‑thinking exploration of what intelligence truly is. He framed the current AI boom as a moment where dazzling empirical successes of deep networks have outpaced our theoretical understanding, prompting a call to move beyond trial‑and‑error tricks toward deductive, principle‑driven explanations. Ma highlighted several core insights: deep learning’s myriad tricks (dropout, batch‑norm, ReLU) are largely phenomenological; the field has been dominated by inductive, data‑driven methods, yet a shift to deductive reasoning could reveal underlying laws. He introduced a biological analogy—phylogenetic intelligence (evolutionary, DNA‑encoded) versus ontogenetic intelligence (individual learning, embodied AI)—suggesting that today’s large language models resemble inherited genetic codes, while embodied agents embody the learning‑through‑experience paradigm. Memorable quotes punctuated the talk: “To understand something, we must create it from first principles,” echoing Feynman, and the striking line, “Just as entropy increases universally, life’s basic law is to structure itself against entropy.” He traced intelligence’s lineage from DNA’s early information storage, through the emergence of eyes and brains, to language and abstract mathematics, underscoring how each milestone reshaped knowledge acquisition. The implications are clear for researchers and industry: a renewed emphasis on principled theory could steer AI toward more robust, adaptable systems, reducing reliance on brittle empirical hacks. Embracing ontogenetic, embodied approaches may yield agents that learn continuously, better mirroring natural intelligence and ultimately delivering safer, more generalizable technologies.