When AI helps you code, who owns the finished product?
Opinion I've been writing software for almost half a century, and my recent experiences with AI suggest that developers may soon find ourselves in a very sticky situation.
I say that having started with 8085 assembly code, then moving on to C, then C++, then Java. Once the web came along I learned the three Ps: Perl, PHP and Python.
Python stuck – more than two decades later it remains my go-to language. I'm far from alone; these days, many introductory computing courses teach Python. This means most scientists and engineers have at least a passing familiarity with it, so when they need to code something, they use Python. Enormous libraries of Python “solutions” can therefore be found online. If you have a coding problem, chances are that someone else has already solved it.
This explains why Python became the de facto language for machine learning and artificial intelligence; researchers working on ML algorithms want to test their hypotheses and optimize their approaches - without having to sweat the details of the code. With Python, researchers don't have to put a lot of effort into their code; instead, they can focus on the problem they're solving. That kicked off a virtuous cycle of development: pretty much everything in artificial intelligence today - except for the lowest-level, tightest loops of bit-banging and matrix multiplications - is written in Python.
