what does it mean for a machine to “learn?” How do we quantify something like skill growth when, for humans, learning is a subjective experience?
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Imagine you living a housing development. Between you and the local shopping center is a dense forest.
To walk around that forest would be a nuisance. So you and your neighbors always try to cut through it, despite the thick undergrowth.
As you and your neighbors keep going shopping, you start to notice paths being formed. It’s a lot easier to walk on a path – even a barely formed one – than it is to trudge through the thickest part of the forest. So over time those paths become more well-trod as people favor them.
Eventually, you’ve got a small number of wide, smooths paths through the forest.
The forest has ‘learned’ the most efficient ways for human beings to travel through it – and that learning is codified in the paths that exist through the forest.
Both human and machine learning work much the same way. At any given time, you’ve got certain features triggering. Your brain – or your computer – learns to correlate those features. The more those features correlate with one another, the easier the paths between them become.
For most people, the difficulty lies in understanding how such a seemingly trivial process that works with a limited number of features can scale up to ‘intelligence’ when you use a massive number of features. But it does.
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