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CrashCourse AI episode 5

Posted on: September 8, 2019


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This week’s episode focused on one example of supervised learning — how AI recognises human handwriting. This is a problem that was tackled quite a while ago (during the rise of tablet PCs) and is a safe bet as an example.

The boiled down basic AI ingredients are:

  1. Labelled datasets of handwriting
  2. Neural network programmed with initial rule sets
  3. Training, testing, and tweaking the rules

The oversimplified process might be: Convert handwritten letters to scanned pixels, allow the neural network to process the pixels, make the neural network learn by comparing its outputs with the labelled inputs, and reiterating until it reaches acceptable accuracy.

The real test is whether the neural network can read and interpret a previously unseen dataset. The narrator demonstrated how he imported and tweaked such data so that it was suitable for the neural network.

My takeaway was not the details because that is not my area of expertise nor my focus. It is the observation that the choice of datasets and how they are processed is key.

If there is not enough data or if there is only partial representation of a larger set, then we cannot blame AI entirely for mistakes. We make the data choices and their labels, so the fault is ours.

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