Interrogating the size of AI algorithms

I have watched so many videos in my journey to understand how artificial intelligence and machine learning work, and one of my favorite YouTube channels belongs to Jordan Harrod. She’s a Ph.D. student working on neuroengineering, brain-machine interfaces, and machine learning.

I began learning about convolutional neural networks in my reading about AI. Like most people (?), I had a vague idea of a neural network being modeled after a human brain, with parallel processors wired together like human synapses. When you read about neural nets in AI, though, you are not reading about processors, computer chips, or hardware. Instead, you read about layers and weights. (Among other things.)

A deep neural network has multiple layers. That’s what makes it “deep.” You’ll see these layers in a simple diagram in the 4-minute video below. A convolutional neural network has hidden layers. These are not hidden as in “secret”; they are called hidden because they are sandwiched in between the input layer and and output layer.

The weights are — as with all computer data — numeric. What happens in machine learning is that the weights associated with each node in a layer are adjusted, again and again, during the process of training the AI — with an end result that the neural network’s output is more accurate, or even highly accurate.

As Harrod points out, not all AI systems include a neural network. She says that “training a model will almost always produce a set of values that correspond or are analogous to weights in a neural network.” I need to think more about that.

Now, does Harrod definitively answer the question “How big is an AI algorithm?” Not really. But she provides a nice set of concepts to help us understand why there isn’t just one simple answer to this question. She offers a glimpse at the way AI works under the hood that might make you hungry to learn more.

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