AI has been already well-researched in 90s.
The improvements in classification algorithms and the early commercialization, such as Siri or FB face recognition are responsible for the current bubble.
No major breakthrough in AI has been made so far. Better classifiers is not AI. They are mere pattern recognition.
AI in particular suffers from poor definitions. Nothing computers can do is "AI", only the things that are still impossible can be called "AI". Doing arithmetic was once thought to be a sign of intelligence. So was playing Chess, or Go, or recognizing faces. Now that computers can do this easily it is no longer considered difficult.
So while the recent progress with neural networks is likely somewhat overhyped you shouldn't discount the importance of making things commercially viable. In the 90s we had software that could do image recognition, true, but it wasn't accurate enough, general enough, fast enough to be useful in practice. This has changed over the last ten years to the point where image recognition has tons of commercial applications.
I'm always baffled by that example. The Industrial Revolution was already ongoing when the steam engines become practical.
This, by the way, would be a major source of additional feedback for better training. Fear has been evolved for a purpose.
I am curious about your definition of 'AI' that seems only obvious to the term 'Artificial'.
The key word here is recognition. Better, faster, more efficient classifiers.
The algorithms for training a better representation of the features, not just features itself is a big deal, but it is, again, supervised learning.
The adversary approach, which gives additional feedback for better training, is mere sophisticated supervised learning.
Take one of the best papers survey (which is an emergent new genre in hipster's blogs) and one will see that there is nothing fundamentally new, apart from sophistication and additional feedback.
In fact, there is a discussion about the definition of unsupervised learning, because of the emergence of techniques like word2vec, where it essentially supervised learning but construct all its tasks from the data itself without any labelling.
CNNs, RNNs, LSTMs, reinforcement learning, gradient descent, backpropagation - all that was used in the 90s, but given the limited computing power they had, people simply couldn't run large enough models on large enough data to get results.