Machine learning and AI are everywhere these days. But the road here was long.

In the early years of computing, researchers struggled to teach machines the difference between the letter B and the number 8. Same loops. Same symmetry. Humans glance once and know the answer. For a computer in the 1970s, it was a puzzle.

Before modern AI, pattern-recognition systems relied on rigid rules—count pixels here, check angles there. The B and 8 fooled those rules again and again. Only when algorithms learned to see context—shape, neighborhood, probability—did the problem fade. Today your phone reads a license plate in the rain at 70 miles per hour. The B8 problem feels quaint.

Runners face their own B8 moments. Two workouts can look nearly identical on paper—same mileage, same pace—yet one builds strength while the other breaks you. The numbers trick you. What separates them is context: sleep, nutrition, stress, weather, your gut feeling. Your body is the AI. It has to see the full picture, not just the digits on your watch.

When I’m training hard for a race, I can’t rely only on charts and paces. I have to notice the tiny cues—tight calves, a restless night, the way a gel sits in my stomach. Miss those signals, and I’m like an old computer, confusing B for 8.

Training, like machine vision, is pattern recognition. We teach ourselves—through miles, failure, and attention—to see what the raw data hides.

Best wishes chasing your running goals,