Just to play devil's advocate for a moment: I'm excited that there will finally be some viable competition for GPUs, but am disappointed that this isn't a gener

Just to play devil's advocate for a moment: I'm excited that there will finally be some viable competition for GPUs, but am disappointed that this isn't a general-purpose multiprocessor. We're long overdue for a general-purpose CPU with say 1024 cores, that avoids a central main memory, where each core can be independently programmed just like any other CPU. Google's may count as a somewhat general-purpose DSP, which is definitely a step forward. But no matter how mature or mainstream a framework like TensorFlow gets, it can never replace full programmability. Without seeing the internals, I'm going to have to give this a nay vote for now. There are many other rather exciting problems that need to be opened up to a new generation of tinkerers. Off that top of my head, it's things like: content-addressable memory to provide high data locality (evolving various interconnects instead of hardwiring them), exploring other types of general vector processing like the kind MATLAB/Octave uses, and exploring other hill-climbing algorithms than backpropagation/neural nets. I picture something more like network topology-agnostic Docker containers programmed in Elixer/Erlang/Go that can act as semi-autonomous agents and switch into various modes in order to solve the problem at hand. I just find that a much simpler metaphor to work with than OpenCL/CUDA/TensorFlow. Yes it would take more silicon and would probably violate YAGNI, but only full programmability gives us the freedom to explore the problem space at the level that's going to be required to implement artificial general intelligence.