[Swizec]

Whenever we figure out how to do something, we stop calling it AI or AGI. If the trend continues, will we eventually have a general AI, but won't consider it anything special? Will it have been just a small incremental step by then?

[bbctol]

I think deep, natural language processing will be unambiguous: if you create a machine that says "Yes, I am intelligent, thanks for asking" in a way indistinguishable from a human, it would be hard to disagree. On the other hand, it's entirely possible that that goal will take so much longer than others we'll have incredibly strong AIs affecting our lives before we notice.

[extesy]

Yes, this is called AI effect: https://en.wikipedia.org/wiki/AI_effect

[kowdermeister]

I think it will need some kind of breakthrough. Current advancements are probably incremental as you stated, but having an AGI might need some new theory we don't have currently.

[xiphias]

Deep learning is the opposite of incremental. For a long time it was not clear whether/how we can learn multi layer networks efficiently. ImageNet changed everything.

[bonoboTP]

Machine learning people basically agree that there weren't any big breakthroughs in deep learning. The success and the hype is mostly a combination of more computing power and more data. The algorithms (convolutional neural network etc.) were invented back in the 1980s and even earlier.

There have been some improvements but they are incremental indeed. More use of ReLU, dropout etc. But it's not a new paradigm at all.

[ebalit]

They weren't any recent breakthroughts. But LSTM and ConvNet are breakthroughts. It just took a lot of times to prove it.

[bonoboTP]

Convnets follow pretty naturally from multilayer perceptrons. Perhaps backpropagation was a breakthrough, enabling the training of ANNs on data, instead of hand-tuning.

But the idea of neural nets is very old, going back to Rosenblatt and connectionism.