Another dot in the blogosphere?

CrashCourse AI episode 4

Posted on: September 1, 2019


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This episode introduced terminology at the heart of neural networks.

  • Architecture: Structure and connections of neurones.
  • Weights: Fine-tuning the computations.
  • Optimisation: Improving architecture and weights.
  • Loss function: Errors that AI makes in predictions.
  • Backpropagation: Providing feedback to weights to improve the computing process.
  • Local optimal solution: Best fit given limited conditions.
  • Global optimal solution: Best fit given better conditions.
  • Learning rate: How much the weights get adjusted during backpropagation.
  • Fitting to training data: Providing relevant information for meaningful output.
  • Overfitting: Allowing AI to find strong but meaningless correlations, e.g., between divorce rates and margarine consumption, or revenue from skiing and death by tangled bedsheets.

Correlation between divorce rates and margarine consumption.

Correlation between revenue from skiing and death by tangled bedsheets.

My guess is that human bias is strongly introduced with weights and training data. This could explain why current facial recognition has problems identifying people with dark skin.

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