> It does this for 20,000 different objects categories
With 15.8% accuracy.
> This is the most powerful AI experiment yet conducted (publicly known).
It's only powerful because they threw more cores at it than anyone else has previously attempted. From a quick skimming of the paper, there does not appear to be a lot of novel algorithmic contribution here. It's the same basic autoencoder that Hinton proposed years ago. They just added in some speed ups for many cores.
It's a great experiment though. You shouldn't detract from its legitimate contributions by making outlandish claims.
That in itself is fairly interesting, it says we can make dramatic improvements just throwing more processing power at the problem. Whatever happens on the algorithms research side of the problem in coming years, you can count on us having access to more processing power.
I think this is the most important aspect of this paper. Throwing more computing power at the problem increases performance significantly. It is possible that our algorithms are adequate but our hardware is not.
With 15.8% accuracy.
> This is the most powerful AI experiment yet conducted (publicly known).
It's only powerful because they threw more cores at it than anyone else has previously attempted. From a quick skimming of the paper, there does not appear to be a lot of novel algorithmic contribution here. It's the same basic autoencoder that Hinton proposed years ago. They just added in some speed ups for many cores.
It's a great experiment though. You shouldn't detract from its legitimate contributions by making outlandish claims.