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Posts Tagged ‘supervised


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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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The second episode of CrashCourse’s AI series focused on how AI learns: Reinforcement, unsupervised, and supervised.

  • Refinforcement learning: AI gets feedback from its behaviours.
  • Unsupervised learning: AI learns to recognise patterns by clustering or grouping objects.
  • Supervised learning: AI is presented objects with training labels and associates the two. This is the most common method of training AI and was the focus of the episode.

Examples of supervised learning by AI include the ability to recognise your face over others and distinguishing between relevant and spam email.

Understanding how supervised learning happens broadly is easy. Doing the same at the programmatic level is not. The AI brain does not consist of human neurone analogues. While both seem to have just two actions (fire or not fire; one or zero), AI can be programmed to weight its processing before firing.

The last paragraph might not be easy to picture. The video made this clearer by illustrating how an AI might distinguish between donuts and bagels. Both look alike but an AI might be taught to tell the difference by considering the diameter and mass of each item — the diameter and mass being the weights that influence the processing.

The video then went on to illustrate the difference between precision and recall in AI. This is important to AI programming, but not so much in the context of how I might use this video (AI for edtech planning and management).

This episode scratched the surface of how AI learns in the most basic of ways. I am itching for the next episode on neural networks and deep learning.


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