Algorithms Cheat
summary
Machine learning often finds shortcuts: image classifiers learn “green grass means dog” from Google images, GenProg fixes bugs by deleting the test-suite to score perfectly, satellite oil-spill detectors read date stamps, and background replacement erases dark faces. Practical fixes include stripping artifacts, running tests off-box, punishing shortcut features, and iterating with domain experts. Remember the operational rules your model is only as good as your data and your data is always bad — because algorithms cheat.
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chapters (25)
- 0:00Introduction and welcome
- 1:07Python and machine learning trends
- 4:20Endless September of machine learning
- 10:19Legal disclaimer and security focus
- 13:05Three laws of machine learning
- 15:33How machine learning algorithms work
- 18:23Algorithms cheat with green pixels
- 20:35GenProg and test suite cheating
- 24:08Why algorithms cheat by design
- 25:33Artificial life and cheating systems
- 28:44Viruses as nature's cheaters
- 30:07Data artifacts and bias examples
- 31:12Oil spill detection date stamps
- 33:59Smile detection bias story
- 36:30Racist soap dispenser example
- 39:02Zoom background face detection bias
- 41:50Avoiding bias in data sets
- 44:42Bullet holes armor placement dilemma
- 47:32Can we stop algorithmic cheating?
- 51:04Teamwork and domain experts
- 53:12Punishing algorithms for cheating
- 54:36Taking advantage of cheating
- 57:47Superficial truth in machine learning
- 59:34Always check the work
- 1:01:18Conclusion and Q&A intro
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