The Decline of Decision Theory in AI: How ImageNet and Academic Incentives Shifted the Field
By
slygent
A baker's-dozen of insight crammed into one ring.
Summary
The article explores why decision theory and mathematical approaches to AI stopped being widely used despite their effectiveness. The author explains that the 2012 ImageNet moment, where deep learning achieved breakthrough results, shifted the AI field's focus toward neural networks and away from mathematically rigorous approaches. This shift was driven by academic incentives, the seductive appeal of not having to explicitly specify objectives, and the allure of 'magical' solutions over systematic mathematical frameworks. The piece reflects on how this transition affected the AI industry and the loss of valuable mathematical tools.
Key quotes
· 4 pulledI'd written a post arguing that what the industry calls 'AI agents' are flowcharts with good marketing, and that the mathematics to do better has existed since the 1960s.
The ImageNet moment in 2012, where deep learning achieved breakthrough results, shifted the AI field's focus toward neural networks and away from mathematically rigorous approaches.
The seductive appeal of not having to explicitly specify objectives, and the allure of 'magical' solutions over systematic mathematical frameworks.
A commenter on LinkedIn replied: 'So why did it stop being widely used?' I sat with this for a day. It deserved a proper answer.
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