This year has started with a lot of deep thoughts about the software 2.0. My conclusion (which is slightly different from Andrej Karpathy’s consideration) is that a software 2.0 is a combination of a Neural network model and its associated weights. This is a concept; now the question is: how to materialize the idea? What artifact represents a software 2.0. I emitted several ideas and tried one of them: to serialize the mathematical model and the weights.

During the past weeks, I’ve had the opportunity to play a bit with Wasm and Go. All those experiments led me to a write a proof of concepts that can illustrate everything I have said recently about: Thinking the deep-learning stack like an Ops (see my post about NNRE/NNDK). Capturing the real value of the training process (the knowledge) into a sequence of bits (the lightning talk I gave about it at the dotAI should be online soon).