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Synthesizing data structure transformations from input-output examples, Feser et al., PLDI’15
The Programmatically Interpretable Reinforcement Learning paper that we looked at last time out contained this passing comment coupled with a link to today’s paper choice:
It is known from prior work that such [functional] languages offer natural advantages in program synthesis.
That certainly caught my interest. The context for the quote is synthesis of programs by machines, but when I’m programming, I’m also engaged in the activity of program synthesis! So a work that shows functional languages have an advantage for programmatic synthesis might also contain the basis for an argument for natural advantages to the functional style of programming. I didn’t find that here. We can however say that this paper shows “functional languages are well suited to program synthesis.”
Never mind, because the ideas in the paper are still very connected to a question I’m fascinated by at the moment: “how will we be developing software systems over this coming decade?”. There are some major themes to be grappled with: system complexity, the consequences of increasing automation and societal integration, privacy, ethics, security, trust (especially in supply chains), interpretability vs black box models, Continue reading