Giving Compass' Take:

• In this Medium post, Cassie Kozyrkov, Chief Decision Intelligence Engineer at Google, discusses the challenges of businesses adopting machine learning and why they do it so poorly: They mistakenly think they need to build things from scratch.

• While machine learning is a big part of innovation in the nonprofit sector, this is a good reminder that most philanthropic organizations are not technology companies: Leave the heavy lifting to the experts, and focus on the main tasks at hand.

• Here are the benefits and drawbacks of machine learning in education.

I’d like to let you in on a secret: when people say "machine learning" it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble.

Imagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that’s the kind of mistake I see businesses making over and over.

If you’re opening a bakery, it’s a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry. You’d also want an oven. While it’s a critical tool, I bet you wouldn’t charge your top pastry chef with the task of knowing how to build that oven; so why is your company focused on the equivalent for machine learning?

Are you in the business of making bread? Or making ovens?

What they don’t tell you is that all those machine learning courses and textbooks are about how to build ovens (and microwaves, blenders, toasters, kettles… the kitchen sink!) from scratch, not how to cook things and innovate with recipes.

Read the full article on why businesses fail at machine learning by Cassie Kozyrkov at Medium.