To the average person, it must seem as if the field of artificial intelligence is making immense progress. According to the press releases, and some of the more gushing media accounts, OpenAI's DALL-E 2 can seemingly create spectacular images from any text; another OpenAI system called GPT-3 can talk about just about anything; and a system called Gato that was released in May by DeepMind, a division of Alphabet, seemingly worked well on every task the company could throw at it. One of DeepMind's high-level executives even went so far as to brag that in the quest for artificial general intelligence (AGI), AI that has the flexibility and resourcefulness of human intelligence, "The Game is Over!" And Elon Musk said recently that he would be surprised if we didn't have artificial general intelligence by 2029. Machines may someday be as smart as people, and perhaps even smarter, but the game is far from over.

This tutorial is on the basics of gradient descent. It is also a continuation of the Intro to Machine Learning post, "What is Machine Learning?", which can be found here. Gradient descent is a method of finding the optimal weights for a model. We use the gradient descent algorithm to find the best machine learning model, with the lowest error and highest accuracy. A common explanation of gradient descent is the idea of standing on an uneven baseball field, blindfolded, and you want to find the lowest point of the field.