Would love to see mention of several of the main contributors to deep learning, such as Geoffrey Hinton, the “father” of deep learning, Andrew Ng and Demis Hassabis in future posts.
On a side note: I lost all respect for andrew ng after the Baidu cheating scandal. https://www.nytimes.com/2015/06/04/technology/computer-scien...
I felt he got away too easy on that, without any apology or even a public statement - especially considering he is a former academic. (And that too he silently deleted his google+ posts.) Imagine if something like that had happened at a Google research team - I am pretty sure Jeff Dean or Peter Norvig would have stepped down.
It did 3 things:
1. Provided a usable solution for what was previously an intractable real world problem, large multiclass image classification, with decent accuracy
2. Crushed the prior benchmark on this task
3. Found a practical workaround to what was the biggest bottleneck, computation time, by utilizing GPUs (and made Nvidia stock explode /s)
The subsequent ImageNet competitions then later provided the perfect catalyst to refining and making deep neural nets mainstream. In parallel the sudden interest from everyone else who in turn started applying neural nets to pretty much every domain out there under the sun, was what I think ultimately made deep learning as it is to what it's today.
I don't understand why you'd want to cheat for a competition like this? I get it, people cheat all the time, but the field of machine learning is built on a foundation of open and shared research, and trust.
It's arguable that they were gaming the system somewhat, but unless a limit was explicitly defined then this just seems like they were doing a lot of exploration in the area.
Imagine if you published some research showing you'd made something that did something cool, but then people lost respect for you because you'd made a lot of previous attempts.
http://www.image-net.org/challenges/LSVRC/announcement-June-...