Participants take part in a skijoring race in Zab, Poland, on January 25, 2026. Breakthroughs, discoveries, and DIY tips sent six days a week. The high-adrenaline winter sport of skijoring, derived from the Norwegian word for "ski driving," takes so many forms that it even defies uniform pronunciation. "If you go to France, it's skijoering, pronounced SKEE-zhor-ing. In German, it's skijöring, pronounced SHEE-yuh-ring," says Loren Zhimanskova, founder of Skijor International and Skijor USA.

The field of machine learning has continued to accelerate through 2019, moving at light speed with compelling new results coming out of academia and the research arms of large tech firms like Google, Microsoft, Yahoo, Facebook and many more. It's a daunting task for the down-in-the-trenches data scientist to keep pace. I advise my data science students at UCLA to be up on the latest research results in order to keep ahead of the pack. I recount how industry luminary Andrew Ng keeps his head above water by toting around a file of research papers (so when he has a free moment, like riding on an Uber, he can consume part of a paper). It does take time to add the research realm to your everyday duties, but I think it's fun to know what technologies are fertile areas of research.

Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias-variance trade-off, appears to be at odds with the observed behavior of methods used in the modern machine learning practice. The bias-variance trade-off implies that a model should balance under-fitting and over-fitting: rich enough to express underlying structure in data, simple enough to avoid fitting spurious patterns. However, in the modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered over-fit, and yet they often obtain high accuracy on test data.