Neural Networks: Explained

Neural Networks, Explained

AI, But Simple Issue #1

In the past decade, the best AI systems (such as image generators, or a machine translation service), have been using a technique called deep learning.

• Deep learning is an approach to AI using neural networks. So how did neural networks get so popular?

Here’s a simple timeline of neural networks:

• Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers.

• Neural networks were a major area of research in both neuroscience and computer science but have had a rough career: dying then resurging, then dying again, but finally resurging fueled by the increased processing power of graphics cards.

Neural networks are a very versatile tool. They are a means of learning how to perform a task by analyzing training examples.

Let’s say you have a function. If you are given this function explicitly, then it is 100% guaranteed to replicate the correct output, given any input.

But what if you weren’t given the function at all, and instead, some data points and wanted to guess what the function was shaped like?

For instance, there is no function that can tell you whether a tumor is malignant or benign. But using neural networks, we can give it some data and tell it to make some sense of it.

If we train it well, then it can predict whether a given tumor is malignant or benign with decent precision.