Tag: deep-learning-history
Before AlexNet the field's best argument against neural networks was that the data was doing the lifting — and Efros had the control experiment to prove it. AlexNet settled that case on the same data everyone else had. The reason to keep the episode is that the modern version of the argument is running now, with the control missing.
Computer vision spent the 2000s carving its best models around the quirks of the datasets it was scored on, and hard negative mining was how it did it — training against your own error cases, one gradient step at a time. The episode is the oldest worked example in this notebook of a measure being optimised until it stops measuring.
Richard Heimann's book reads the reconstructed reading list Ilya Sutskever gave John Carmack as an argument rather than a bibliography. Chapters 1 and 2 give the four-part worldview it claims to encode, and then spend a chapter showing that AlexNet invented almost nothing — which is the part this notebook cares about.
OpenAI withheld GPT-2 in February 2019 on the grounds of risks that a later post admitted had not materialised, and the justification pointed at future systems rather than at the model being withheld. The argument structure — act early because acting late is worse — is the one that later produced the board crisis, and it is unresolved in exactly the same way today.