The 60-Year Hunt for AI's Most Important Function

Every modern AI model relies on activation functions to build complex models.
But what activation functions work and why?

In this video, join me to trace the full evolution.

00:00 Activation function intro
00:26 Feedforward networks in a Transformer
01:55 Heaviside step function
02:26 Sigmoid: differentiable and bounded
02:53 Sigmoid challenge 1: Constrained gradient updates
05:54 Sigmoid challenge 2: Vanishing gradient problem
07:49 Tanh: Zero-centering the signal
09:29 ReLU: the breakthrough
10:29 The "dying ReLU" problem
11:28 Leaky ReLU and Parametric ReLU
12:53 New perspective: content and gate
14:15 GELU and Swish
16:14 Gated Linear Unit: GLU, ReGLU, GEGLU, and SwiGLU
20:20 Adjusting hidden dimension for GLU-based FFN.
21:55 Squared ReLU: Sparse and efficient
23:04 Implementation review

Video made with Manim: https://www.manim.community/

Видео The 60-Year Hunt for AI's Most Important Function канала Jia-Bin Huang