This article says the three breakthroughs were: parallel computation, big data and better algorithms. I think we have a tendency to simplify scientific achieve

This article says the three breakthroughs were: parallel computation, big data and better algorithms. I think we have a tendency to simplify scientific achievements. There's this romantic notion that real progress happens through eureka moments. Each of these "breakthroughs" are made up of many separate discoveries/inventions that, when aggregated, lead to modern artificial intelligence. So the real question is - what types of new discoveries do we need to keep advancing AI? I'm not an expert in the field, but here are my guesses: - Transfer learning is huge. How can we apply the data used from one problem to solve a different problem? - Generalized pattern matching. Can we use the same algorithm that identifies separate objects in vision to identify different noises? Can we map these problems to the same dimensions? - Better training sets. It takes years for a child to learn object permanence, much less speaking, listening and walking. What data sets can we feed a computer for it to learn about the real, non-virtual world around it? - NLP based off of learning from real world datasets. Can we give a computer data from Google Glass and let it learn edge detection and words and then applying those words to the things it sees? Perhaps progress in NLP will come teaching a computer words the way you would a child instead of hard coding definitions. If you like Wittgenstein, I'd say it's a move away from Tracatus and towards Philosophical Investigations.