Garry Kasparov, Ke Jie….Bill Belichick?
The Measurement Minute with Gary Angel If you consider the factors that control what makes a good ML problem and apply them to major sports leagues, it's pretty clear what role stands ou...
Scale Efficiency and ML – Why Our Short-Attention Span Matters
The Measurement Minute with Gary Angel Thinking about when to use machine learning? Defined outcomes and clock-time efficiency are both important. But so too is the scale of the problem ...
McDonald’s Just Bought Dynamic Yield for $300 Million – It’s not your typical acquisition
The Measurement Minute with Gary Angel Acquisitions are a dime (or $300 million) a dozen these days. But McDonald's acquisition of Dynamic Yield is definitely an out-of-the-box strategic...
The Surprising Role for Humans in Creating ML Training Data
The Measurement Minute with Gary Angel Creating training data for ML can be really hard. That's why you should consider using the data you have to generate more. Check out the full blog ...
ML Use-Cases without a Baked-in Outcome : Geolocation and Associate Detection
The Measurement Minute with Gary Angel The use-cases we here at Digital Mortar have pushed on with ML all involve business problems where we don't have defined outcome in our data. In th...
Supervised Learning and its Challenges
The Measurement Minute by Gary Angel There's a lot of hype about ML - some of it deserved. But when you look at it closely, almost all of the techniques that really matter in ML fall int...
Breaking Up Is Hard to Do
The Measurement Minute w. Gary Angel The evolution of big tech didn't go the way most of us expected. And the creation of giant monopolists (or monopsonists) creates real issues for publ...
Why Machines Learn Faster AND Slower than You Do
The Measurement Minute with Gary Angel When you see ML programs beating the world's best chess or Go players, it's easy to assume that machines are faster learners than people. They're r...
When Machine Learning Works
The Measurement Minute with Gary Angel : When Machine Learning Works Machine Learning (ML) has some signal successes. But maybe not in your enterprise. That could be because you suck at ...