In my opinion, their target audience are scientists rather than programmers, and a scientist most often think of code as a tool to express his ideas (hence, perfect AI generated code is kind of a graal). The faster he can express them, even if the code is ugly, the better. He does not care to reuse the code later most of the time.
I have the hint that scientists and not programmers are the target audience as other things may trigger only one category but not the other, for example, they consider Arduino a language, This makes totally sense for scientists, as most of the ones using Arduino dont necessarily know C++, but are proud to be able to code in Arduino.
Sure, but their tools are complexity management tools: Hypotheses, experiments, empirical evidence, probabilities. To my knowledge, they deal far less with the determism programmers rely on. It's reproducible if you get similar results with the same probability.
I like programming, I like clean code, so it's something I struggled with when I began research.
But actually, producing easy to read code when you don't have specifications, because you don't know yet if the idea will work, and you are discovering problems on that idea as you go doesn't lead to readable code naturally.
You refactor all the time, but then something that you misunderstood becomes a concern, and you need to refactorer again everything, and again and again.. You loose much time, and research is fast paced.
Scientists that spend too much time cleaning code often miss deadlines and deliverables that are actually what they need to produce. Nobody cares about their code, as when the idea is fully developed, other scientist will just rewrite a better software with full view of the problem. (some scientists rewrite their full software when everything is discovered)
I think a sensible goal would be easy to write code instead of easy to read for scientists.
But if you are iterating on code and using an LLM without even looking at the code, there's a reasonable chance that when you prompt "okay, now handle factor y also", you end up with code that handles factor y but also handles pre-existing factor x differently for no good reason. And scientific work is probably more likely than average programming to be numerics stuff where seemingly innocuous changes to how things are computed can have significant impacts due to floats being generally unfriendly.
Totally agree, in my experience we are far from having reliable research code based on prompts.
We are clearly not there yet, but I feel that the article is pushing in that direction, maybe to push research in that direction.
There was a long time ago an article from the creators of Mathematica or maple, I don't remember that said something similar. The question was: why do we learn about matrix operations at school, when (modern) tools are able to perform everything. We should teach at school matrix algebra and let students use the software (a little bit like using calculators). This would allow to make children learn more abstract thinking and test way more interesting ideas. (if someone has the reference I'm interested)
I feel the article follow the same lines. But with current tools.
(of course I'm skipping the fact that Mathematica is deterministic in doing algebra, and LLMs are far from it)
In my opinion, their target audience are scientists rather than programmers, and a scientist most often think of code as a tool to express his ideas (hence, perfect AI generated code is kind of a graal). The faster he can express them, even if the code is ugly, the better. He does not care to reuse the code later most of the time.
I have the hint that scientists and not programmers are the target audience as other things may trigger only one category but not the other, for example, they consider Arduino a language, This makes totally sense for scientists, as most of the ones using Arduino dont necessarily know C++, but are proud to be able to code in Arduino.