Software 2.0 implies that machines are better at figuring a way to solve a problem. All you have to do is teach them through data
Data annotation tools will drive ML models. Tuning to perfection will require advanced tooling
AI and machine learning are being used to help developers through every stage of the software development lifecycle
Machine learning requires large amounts of data, usually in the form of labeled examples. We have come to a point where we can stop thinking of programming as writing a step of instructions in a programming language like C or Java or Python. Instead, we can program by example. We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. In short, we can use machine learning to automate software development itself.
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