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Programming with AI, so far, tries to specify something precise, algorithms, in a less precise language than what we have. It's the difference between Euclid a

Programming with AI, so far, tries to specify something precise, algorithms, in a less precise language than what we have. It's the difference between Euclid and modern notation, with AI programming being like Euclidean notation and current programming languages being the modern notation: "if a first magnitude and a third are equal multiples of a second and a fourth, and a fifth and a sixth are equal multiples of the second and fourth, then the first magnitude and fifth, being added together, and the third and sixth, being added together, will also be equal multiples of the second and the fourth, respectively." versus a(x + y) = ax + by If AI programming can find a better way to express the problems we're trying to solve, then yes, it could work. It would become a matter of "how well the compiler works". The current proposals which use natural language as the notation is not better than what we have.

lmpdev 2y ago
The only problem with this is the fact that the 99%+ of issues with software products aren’t the fact that a parsimonious language was used and tightly coupled to the compiler The vast majority of issues is missed edge cases between what the user wants and expects, and the design and function of the software Higher productivity would in theory allow programmers more opportunities to address more issues Programs don’t exist in a vacuum, users and other actors need to interact with them Whether or not these LLMs result in increased productivity with the same or better quality is a more pertinent question