Behind every great deep learning framework is an even greater programming languages concept

In many areas, such as image recognition, natural language processing, search, recommendation, autonomous cars, systems software and infrastructure, and even Software Engineering tools themselves, Software 2.0 (= programming using learned models) is quickly swallowing Software 1.0 (= programming using handcrafted algorithms). Where the Software 1.0 Engineer formally specifies their problem, carefully designs algorithms, composes systems out of subsystems or decomposes complex systems into smaller components, the Software 2.0 Engineer amasses training data and simply feeds it into an ML algorithm that will synthesize an approximation of the function whose partial extensional definition is that training data. Instead of code as the artifact of interest, in Software 2.0 it is all about the data where compilation of source code is replaced by training models with data. This new style of programming has far-reaching consequences for traditional software engineering practices. Everything we have learned about life cycle models, project planning and estimation, requirements analysis, program design, construction, debugging, testing, maintenance and implementation, … runs the danger of becoming obsolete.