In my opinion, in the 80-90's, neural networks and machine learning used to be 10% a solid concept in terms of academic research and 90% hype. Now neural networ

In my opinion, in the 80-90's, neural networks and machine learning used to be 10% a solid concept in terms of academic research and 90% hype. Now neural networks and machine learning are 10% a solid concept in terms of being a practical applicable tool and 90% hype. Things have changed a lot because I almost run out of fingers when trying to express the orders of magnitude in which raw processing power has increased. You can literally feed the network with anything when training and get reasonable results later in recognition. That's one impressive yet humanly vague hash table there. And no, you don't have to wait for months or weeks anymore to train new things. Not even days, necessarily. Why people pull in artificial intelligence is both naively optimistic and quite understandable. Modelling something of a neural system is so close to how biological brain works that the parallel is blatantly obvious. On the other hand, the current deep networks do not translate to intelligence; not at all. Machine learning might be, in part, something we could describe as "intelligent" as it's able to connect dots that are very difficult to connect by traditional algorithms but it absolutely is no intelligence. Then again, we do hang out in the same neighbourhood. If we will ever create an artificial intelligence in software I'm quite certain it will be very much based on some sort of massively deep and parallel network of dynamic connections. I'm not that interested in artificial intelligence myself. I would be interested in artificial creativity and emotional senses, but to model those there are bigger metaphysical questions to be answered first.