Software 2.0: Neural Codebases and End-to-End Differentiable Programming
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I6P101Keywords:
Software 2.0, Neural Codebases, Differentiable Programming, Deep Learning, End-To-End Learning, Program Synthesis, Machine Learning, AI Software DevelopmentAbstract
Software 2.0 is a method paradigm change that involves software systems design, development and maintenance. The shift in traditional rule-based, hand-coded programming to new machine-learned tasks and its model, Software 2.0 uses the neural network and differentiable programming to develop smart, flexible and generalizable systems. The evolution is based on the fact that deep learning, automatic differentiation, and large-scale data have converged. The application of the emergent field lets neural codebases be programs encoded in weights of neural networks and should, in principle, allow software systems to be trained end-to-end directly on data. This essay outlines the concepts behind Software 2.0 and explains its procedures, consequences and uses. Essential aspects are neural architectures, differentiable interpreters, training pipeline, optimisation techniques, and practical applications like autonomous vehicles, language models, and programs synthesis. We explore the pathways to shift to gradient-based optimisation and explore toolchains such as PyTorch and JAX, and evaluate their running time and memory usage as well as maintainability. The findings indicate a good future of Software 2.0, even though there still exist issues of transparency, generalization and safety. Possible futures of this generation of programming are hybrid models as well as neurosymbolic systems and what an ethical framework will look like is also outlined in the paper
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