Evolution of Intelligence
Life survives by learning to predict its future
Collective Intelligence
‣ Language ‣ Sharing ‣ Craft
Artificial Intelligence
‣ Logic ‣ Simulation ‣ Learning
Stone Age��Communicate
Renaissance��Reason
Digital Age��Compute
Adapted from Prof. Yi Ma (Slides 20-28)
The Decade of Origin => Machine Learning
1940s — Laying the Groundwork for Machine Intelligence
Learning from Nature: Neurons & Nets
1888 Golgi & Cajal
�Mapped the neuron’s dendrites
1943 McCulloch & Pitts
�First math model (perceptrons)
1959 Hubel & Wiesel
�Visual cortex
1980 Fukushima & 1989 LeCun
�Added convolution + pooling
History of Machine Learning
Figure credit: Prof. René Vidal
Modern Evolution of Deep Neural Networks
Credit: Prof. Yi Ma
The Future: Black-Box to White-Box AI
Modern AI systems are still heuristic black boxes—blocking explanation, safety guarantees, and rapid iteration—which is particularly concerning as these systems are growing to be widespread and used in high-stakes applications that demand interpretability.
What to Learn?
How to Learn?
Why Correct?
What is Intelligence?
Definition: An intelligent system is one that has the mechanisms for self-correcting and self-improving its existing knowledge (or information).
If your “speed of learning” (intelligence) stays high for long, your “distance traveled in learning” (knowledge) piles up. Conversely, at any moment the slope of your knowledge-vs-time graph—that is, how steeply it’s climbing—represents your intelligence.
A system without self-correction or self-improvement—no matter how large—lacks intelligence.
VS
Who has intelligence, �Who has knowledge?
Winner
The Era of Heros
1940s�“Animal” Intelligence
1956�Unique to Human
Today’s AI�Animal ∨ Human?