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> I know nothing about pathology, but in terms of software, I think slower adoption to new tech is what we need, especially when the "new tech" is just a 5% fas

> I know nothing about pathology, but in terms of software, I think slower adoption to new tech is what we need, especially when the "new tech" is just a 5% faster javascript framework. I hope that's not the definition people are using when discussing adoption of "new tech". When it comes to the topic of AI and "new tech adoption", I think about something like the Rust programming language. I apologize if it chafes the people reading this comment that I'm something of a Rust evangelist and I'm working from a point of view that Rust's very existence is a (large) net-positive when it comes to programming and how we think about programming language design. My fear with AI tools in their current state is that it will slow down innovation in programming languages. Rust gained popularity because it brought things to the table that made writing safe, performant, and correct (thinking about the strong, expressive, static type checking) software much easier than it had been with the old incumbents (in certain domains). But, if Rust were released today or in the near future, would it take off? If we could, hypothetically, get to a point where an AI tool could spit out C or C++ code and push it through some memory sanitzers, Valgrind, etc and just iterate with itself until it was very likely to be free of memory safety bugs, why would we need a new language to fix those things? I guess we wouldn't. And it wouldn't really matter if the code that gets generated is totally inscrutable. But, it saddens me to think that we might be nearing the end of human-readable programming language research and design.

acomjean 2y ago
It will be harder for new languages and frameworks. The AI Its exasperates the bootstrapping problem. An interesting example is perl which is essentially static at this point (perl 6 was renamed and never got traction). I know from experience running pipelines that those old perl scripts almost always work, where if I come across an old python script (2x) I will have to go in and make some adjustments. Maybe a library has changed too… People like new shinny things though. Maybe the new languages will try to train the ai and release there own models, but that’s a huge lift.
- PaulHoule 2y ago
  Might be easier than you think. If DeepSeek can train a model cheaply, so you can you. Probably more cheaply as the technology and models get better. People used to be worried that AI performance was going to degenerate if models are trained on AI slop, but it's been found that synthetic data is the bee's knees for coding, reasoning and such, so it may well be that a new language comes with a large amount of synthetic examples which will not just be good for AI training but also for documentation, testing and all that.
ANewFormation 2y ago
All you're talking about there in the end would be another compilation step. I'm highly bearish on the concept of anything like that ever being possible (and near perfectly reliable) with llms, but if it were then it'd make sense as just another processing phase in compilation.
PaulHoule 2y ago
I'm also going to argue that Rust is a less AI-friendly language than, say, Go. GC languages have many benefits that come from 'you don't have to think about memory allocation'. For instance you can just smack an arbitrary library into a Java program with maven and not think at all about whether the library or the application is responsible for freeing an object. The global problem of memory allocation is handled by a globally scoped garbage collector. LLMs are great at superficial/local translation processes (e.g. medium-quality translation of 中文 to English doesn't require constraint solving any more than remembering which of the many indexing schemes is the right one for 'how do I look up this row/column/whatever in pandas') But fighting with the borrow checker (getting global invariants right) is entirely outside the realm of LLM competence.

acomjean 2y ago

It will be harder for new languages and frameworks. The AI Its exasperates the bootstrapping problem. An interesting example is perl which is essentially static at this point (perl 6 was renamed and never got traction). I know from experience running pipelines that those old perl scripts almost always work, where if I come across an old python script (2x) I will have to go in and make some adjustments. Maybe a library has changed too… People like new shinny things though. Maybe the new languages will try to train the ai and release there own models, but that’s a huge lift.

PaulHoule 2y ago
Might be easier than you think. If DeepSeek can train a model cheaply, so you can you. Probably more cheaply as the technology and models get better. People used to be worried that AI performance was going to degenerate if models are trained on AI slop, but it's been found that synthetic data is the bee's knees for coding, reasoning and such, so it may well be that a new language comes with a large amount of synthetic examples which will not just be good for AI training but also for documentation, testing and all that.

ANewFormation 2y ago

All you're talking about there in the end would be another compilation step. I'm highly bearish on the concept of anything like that ever being possible (and near perfectly reliable) with llms, but if it were then it'd make sense as just another processing phase in compilation.

PaulHoule 2y ago

I'm also going to argue that Rust is a less AI-friendly language than, say, Go. GC languages have many benefits that come from 'you don't have to think about memory allocation'. For instance you can just smack an arbitrary library into a Java program with maven and not think at all about whether the library or the application is responsible for freeing an object. The global problem of memory allocation is handled by a globally scoped garbage collector. LLMs are great at superficial/local translation processes (e.g. medium-quality translation of 中文 to English doesn't require constraint solving any more than remembering which of the many indexing schemes is the right one for 'how do I look up this row/column/whatever in pandas') But fighting with the borrow checker (getting global invariants right) is entirely outside the realm of LLM competence.