Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson’s adage applies well to AI adoption: The future is already here, it’s just not evenly distributed.
How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist
Having an intuition for how machine learning algorithms work — even in the most general sense — is becoming an important business skill. As Andrew Ng has written: “Almost all of AI’s recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B).” But how does this work? As you might imagine, many exciting machine learning problems can’t be reduced to a simple equation like y = mx + b. But at their essence, supervised machine learning algorithms are solving for complex versions of m, based on labeled values for x and y, so that they can predict future y’s from future x’s. If you’ve ever taken a statistics course or worked with predictive analytics, this should all sound familiar: It’s the idea behind linear regression, one of the simpler forms of supervised learning.