Neural Networks and Its Discontents Part 1: From Backpropagation to Forward-Forward Algorithm

🦊 Welcome to Part 1 of our exploration into the fascinating world of backpropagation and its limitations in the realm of artificial intelligence. In this two-part series, we breifly delve into the linchpin of AI learning, its significance, and the emergence of a promising alternative: the forward-forward algorithm.

Please stay tuned for Part 2, where we will dive deeper into the forward-forward algorithm and explore the groundbreaking insights shared by the renowned AI pioneer, Geoffrey Hinton. In EPISODE #112 of Eye-on.AI’s podcast, Hinton provides a comprehensive explanation of his new learning algorithm, the forward-forward algorithm, which offers a fresh perspective on how the cerebral cortex might learn.

Backpropagation: The Brain of Artificial Intelligence

A Simple Analogy to Understand Backpropagation

Imagine you’re playing a game of bowling. Your goal is to knock down as many pins as possible. When you roll the ball, it doesn’t quite hit the center, and you miss a few pins. What do you do next? Naturally, you would adjust your next roll based on how the previous one went. If the ball went too far to the right, you’d aim a little more to the left on your next throw, and vice versa.

This is an intuitive explanation of backpropagation. In the world of AI, our neural network is like…