Another dot in the blogosphere?

CrashCourse AI episode 2

Posted on: August 18, 2019


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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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