About this episode

This episode explores the failures of AI systems at the edges, focusing on edge cases and the implications of rare events in machine learning.

Why Your AI Works Perfectly Until It Doesn't Edge Cases, Blind Spots and the Failures Nobody Tests For 🤖 Every AI system has a comfortable middle and a neglected edge. In the middle everything works: the typical customer, the standard query, the well-lit product photo. At the edge sits everything else, and that is where artificial intelligence quietly, confidently falls apart. This episode is about edge cases, the rare and ambiguous situations no dataset fully contains, and why they are not a bug to be patched away but a permanent feature of how machines learn. 🐱 We start with a model that called a cat in a knitted jumper a loaf of bread with 94% confidence, then unpack the machinery behind such failures: why rare events are only rare individually while being collectively constant, why confidence scores measure plausibility rather than understanding, why models take shortcuts (the wolf classifier that had actually learned to spot snow), and why data drift makes healthy systems rot without anyone noticing. 🚗 Then the stakes rise. The case study examines the fatal 2018 Tempe crash involving an Uber self-driving vehicle and Elaine Herzberg, using the official NTSB report…

People in this episode

Host: Dietmar Fischer

Topics covered

• AI failures
• edge cases
• machine learning
• self-driving vehicles
• facial analysis
• data drift

Keywords

• edge cases
• AI failures
• self-driving cars
• data drift
• facial recognition
• confidence scores

Mentioned in this episode

Organizations: Uber, NTSB

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