When is say analytical, i mean in the sense that mathematics can be used. By optimization, i mean a set of parameters that are tuned by minimizing a function with computers.
On computational power, we'll have to to agree to disagree. I believe self-driving cars and ng's helicopter are examples of why i think computing power is sufficient, there are many others (how kinect,google goggles,ibm watson etc etc. were built. There are also the "humie" awards in genetic programming which compete with traditional engineering). In engineering, finite element analysis has taken over. It is more human intensive than straight up machine learning but it's an example nevertheless of compute power displacing humans. I expect finite element analysis to be overtaken by machine learning too in many of it's applications as awareness/trust in machine learning gains mindshare.
Really, my basic point is, forget what you know, start afresh, and put faith in brute force searches. In natural language processing this happened 20 years ago and they made great progress, in computer vision this is happening right now, and these two fields are the toughest in my humble opinion. What they do, surely other fields can learn from.
On computational power, we'll have to to agree to disagree. I believe self-driving cars and ng's helicopter are examples of why i think computing power is sufficient, there are many others (how kinect,google goggles,ibm watson etc etc. were built. There are also the "humie" awards in genetic programming which compete with traditional engineering). In engineering, finite element analysis has taken over. It is more human intensive than straight up machine learning but it's an example nevertheless of compute power displacing humans. I expect finite element analysis to be overtaken by machine learning too in many of it's applications as awareness/trust in machine learning gains mindshare.
Really, my basic point is, forget what you know, start afresh, and put faith in brute force searches. In natural language processing this happened 20 years ago and they made great progress, in computer vision this is happening right now, and these two fields are the toughest in my humble opinion. What they do, surely other fields can learn from.