Like speech recognition, or machine vision, or predictive analytics. What's awesome is that deep learning seems to be coming out with the best results across many different disciplines and problem domains, leading to optimism about it being a general technique that can be applied to many different types of complex problems.
So yea, modern renaissance of machine learning and AI is not the same as the promise of strong AI, very opposite.
I am totally with you in that the future is hard to predict. But this isn't quite the same thing, there are notable differences here that'd suggest that this isn't like the promises made by the community since last 50 years.
Speaking specifically about deep learning, never before in history have we been able to work with the sheer volume of data that we now have and can easily work with. GPU computing shits on the CPU and is faster than the CPU by orders of magnitudes for things like matrix multiplication[1]. Advances in the field like dropout, transfer learning, ensemble learning, boosting, convolutional neural networks, unsupervised pre-training, etc have also led to breakthroughs.
Finally, researchers have more access to large freely available datasets like ImageNet, which has had an enormous impact on the field of machine vision. Freely available tools like Caffe, TensorFlow, Theano, Lasagne, Keras, Torch also make it easy for engineers and not machine learning experts to utilize the state of the art techniques to build awesome software.
