Neurons

Neural Networks: Origins & Evolution

• Historical Development

• 1950s: Initial development
• 1980s-1990s: First popularity wave
  • Success in “handwritten digit recognition”
    • Postal codes
    • Bank check amounts
• 2005-Present: Major resurgence
  • Rebranded as “deep learning”
  • Revolutionary impacts:
    • Speech recognition
    • Computer vision (ImageNet 2012)
    • Natural language processing
    • Climate change
    • Medical imaging

• Biological Inspiration vs Reality

• Original goal: “mimic how the biological brain learns”
• Modern neural networks
  • “Almost nothing to do with how the brain learns”
  • Shifted focus to engineering principles
  • Limited understanding of actual brain function
    • Neuroscience still making fundamental breakthroughs

• Why Neural Networks Succeeded Recently

• Data availability
  • Digitalization of society
  • Internet and mobile devices
• Computing advances
  • GPUs originally for graphics
  • Enabled processing of large datasets
• Performance scaling
  • Traditional ML plateaus with more data
  • Large neural networks “performance will just keep going up”
  • Can handle “big data” effectively

Neural networks, while originally inspired by biological brains, have evolved into powerful engineering tools that excel with large datasets. Their success stems from the combination of increased data availability and computational power, rather than biological accuracy.