Machine learning has come a long way. The origins of neural networks date back to the 1950s, modeled after the neural connections in the human brain. Early neural nets could only solve basic problems.

The Rise of Deep Learning

Today, improved algorithms and vast datasets enable much more complex "deep" neural networks. Modern nets have many layers of interconnected nodes - hence the term "deep learning". With multiple layers, they can solve increasingly sophisticated tasks.

GPU Power Unlocks AI Progress

What allowed the jump to deep learning? The graphical processing unit (GPU). GPUs can perform massively parallel calculations, ideal for powering neural networks with millions of parameters. As GPUs became more affordable thanks to demand from video gaming, researchers could train more intricate models.

AI Starts Rivaling Humans

Fuelled by GPUs, revolutionary AI applications appeared: image recognition on par with humans; near-perfect speech transcription; language translation between scores of languages. Machines can now write, create art, diagnose disease and more thanks to deep learning.

Ongoing AI Breakthroughs

State-of-the-art techniques like transformers and reinforcement learning drive the latest innovations. AI research company DeepMind is pioneering algorithms closer to general learning abilities. Each year AI systems match or surpass humans on new tasks.

The Road to Artificial General Intelligence?

While current AI excels at narrow applications, researchers continue pursuing artificial general intelligence - mastering nearly any task at a human level. Modern machine learning hints at a path eventually leading to AGI, though the timeline remains speculative. Still, each AI leap fuels hopes for replicating something approaching human thinking.