I'll show you how to use machine learning to analyze your performance in your sport of choice (mine's Tennis!). Can you ever forget the first time you grab that pass, fly down the court, and sink that puck right through the net as your fans yell adoringly from the bleachers, TOUCHDOWN! That's what you get when you spend your high school years learning calculus and icing pi on cookie cakes instead of doing sports. How many friends do you think this made me? It's time you never get back.

In this post, I'll show you how to build an AI-powered, searchable video archive using machine learning and Google Cloud–no experience required. One of my favorite apps ever is definitely Google Photos. In addition to backing up my precious pics to the cloud, it also makes all of my photos and videos searchable using machine learning. So if I type "pool" in the Photos app, it returns all everything it recognizes as a pool: This is all well and good if you just want to use somebody else's software. But on this website, we build our own PCs, store our own encryption keys, churn our own butter, and build our own Google Photos Videos app.

In a recent blog post, Google announced enhancements to a part of its Vision AI portfolio: AutoML Vision Edge, AutoML Video, and the Video Intelligence API. Each received updates to enhance their capabilities. Both AutoML Vision Edge and AutoML Video were both introduced earlier this year, in April, as a part of Google's AI Platform, while the Video Intelligence API introduction dates back a few years prior, with a public beta release in June 2017. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. With AutoML Vision Edge, developers can train, build and deploy ML models at the edge.

Several weeks ago, Jefferies' analyst James Kisner published a scathing report, shedding light onto the shortcomings of IBM Watson. Kisner focused on the $60 million disastrous Watson project for MD Anderson, and highlighted how much IBM is lagging behind Amazon and Apple. As John Mannes pointed out on TechCrunch, "things would look much worse if Google, Microsoft and Facebook were added to this table." He also eloquently summarized the common pitfall in our approach to AI: "Reality is that AI isn't an amorphous black hole that sucks in unstructured data to produce insights. A solid data pipeline and a domain-specific understanding of the AI business problem at hand is table minimum."