Yes, "they had all that" in 1992: speech recognition, image classification, speech synthesis etc. These are problems as old as the field [1]. They didn't work a

Yes, "they had all that" in 1992: speech recognition, image classification, speech synthesis etc. These are problems as old as the field [1]. They didn't work as well as they do now, but computers where not as powerful and datasets where not as large, as they are now. The approaches though, that do all these things today, are still the same old statistical learning approaches, just with more data and more compute. At the same time, we don't know if what statistical approaches can't do, but symbolic approaches excel at, like reasoning, for example, would also benefit from the modern advances in computational power, because there's almost nobody trying. All the large tech corporations are head-over-heels for statistical learning and most people are running behind them, following the current trend. So nobody's trying to scale up, I don't know, classical planning or SAT solvers, to the extent that Google, Facebook et al, have scaled up neural nets. _______________ [1] Specifically, the field of pattern recognition, which is older than machine learning as a field of research. See the Introduction chapter in "Statistical Learning Theory" (the textbook) by Vapnik for a quick run-through of the history of statistical inference, pattern recognition and statisical learning.