Mass production of AI solutions

AI will change how we live in the next 30 years, but it's still currently limited to a small group of companies. In order to scale the impact of AI across the globe, we need to reduce the cost of building AI solutions, but how? Nate Keating explains how to apply lessons learned from other industriesspecifically, the automobile industry, which went through a similar cycle.

AI will change how we live in the next 30 years, but it’s still currently limited to a small group of companies. In order to scale the impact of AI across the globe, we need to reduce the cost of building AI solutions, but how? Nate Keating explains how to apply lessons learned from other industries—specifically, the automobile industry, which went through a similar cycle. The first car was invented in the eighteenth century, and the first electric car was invented in 1888. It took more than a hundred years to mass produce electric cars. Cars become “democratized” when Ford invented the moving assembly line. Ford optimized the manufacturing process, making cars affordable to the middle class. While AI faces slightly different challenges than the automobile industry, companies like Facebook (FBLearner Flow), Uber (Michelangelo), and Google (TFX) have similarly built internal machine learning “assembly lines” that supercharge AI advancement. Nate explores options and characteristics of those systems as well as the value of a publicly available machine learning assembly line.