Yi Ma - Pursuing the Nature of Intelligence

Why It Matters

Understanding intelligence as a principled, entropy‑fighting process redirects AI research from fleeting tricks to robust, adaptable systems, accelerating the development of truly general and reliable artificial intelligence.

Key Takeaways

• Deep learning's empirical tricks lack principled theoretical explanations.
• Shift from inductive trial‑and‑error to deductive reasoning urged.
• Intelligence viewed as life’s mechanism combating entropy through adaptation.
• Phylogenetic vs. ontogenetic intelligence parallels big models and embodied AI.
• Historical milestones from DNA to language shape modern AI foundations.

Summary

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.

Original Description

Biography:

Yi Ma is a Chair Professor in Artificial Intelligence, the inaugural director of the School of Computing and Data Science and the Institute of Data Science of the University of Hong Kong since 2023. His research interests include computer vision, high-dimensional data analysis, and integrated intelligent systems. Yi received his two bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two master’s degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000. He served on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He was on the faculty of UC Berkeley EECS Department from 2018-2023, where he continues to be a visiting professor. He has published over 65 journal papers, 150 conference papers, and four textbooks on 3D vision, generalized PCA, high dimensional data analysis, and machine intelligence. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.

Abstract:

In this talk, we will try to clarify different levels and mechanisms of intelligence from historical, scientific, mathematical, and computational perspective. From the evolution of intelligence in nature, from phylogenetic, to ontogenetic, societal, and to scientific intelligence, we will try to shed light on how to understand the true nature of the seemingly dramatic advancements in the technologies of machine intelligence in the past decade. We achieve this goal by developing a principled theoretical framework to explain deductively the practices of deep representation learning from the first principle of pursuing low-dimensional structures in data distributions. This framework not only reveals true nature hence both capabilities and limitations of the current deep architectures, and but also provides principled guidelines to develop more complete and more efficient learning architectures and systems. Eventually, we will clarify the difference and relationship between Knowledge and Intelligence, which may guide us to pursue the goal of developing systems with true intelligence, at least at the level with a predictive and generative memory. If time permits, we will also showcase some of the ongoing new technological developments towards realizing intelligence within an open real physical world.

EECS Colloquium

Wednesday May 6, 2026

306 Soda Hall (HP Auditorium)

4 - 5p