Perceptron: Understanding AI
A short history of AI, from the 1958 Perceptron to today's deep learning.
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To understand today’s AI, I go back to where it started. Beginning with the Perceptron, the first practical neural network, I trace how machines learned to recognise patterns and the breakthroughs that made modern deep learning possible.
Key takeaways
- The Perceptron (1958) showed a machine could learn from labelled data.
- Backpropagation in the 1980s made multi-layer networks trainable.
- GPUs supplied the parallel computing power that large neural networks need.
- Networks grew from AlexNet’s 8 layers in 2012 to today’s models with far deeper architectures and huge parameter counts.