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I think what you’re missing in a lot of these comments is that AI doesn’t need to operate at the peak of human intelligence like Ramanujan in order to absolutel
by Uehreka 4y ago
I think what you’re missing in a lot of these comments is that AI doesn’t need to operate at the peak of human intelligence like Ramanujan in order to absolutely gut a lot of job markets.
Like, most developers aren’t writing proofs that a series converges to pi or building Apollo modules where everything has to work perfectly. I’m not gonna act like what we do is easy, because it’s not. However I’ll never forget the day I wrote a WebGL shader in GLSL with Copilot’s help, and then clicked over from a GLSL file to a JS file and Copilot immediately wrote the code to create a THREE.ShaderMaterial, import my shader (with the right file path) and filled out the uniform names and types perfectly on the first try. It took under 2 seconds, and at the datacenter maybe a fistful a watts, and it left me fucking speechless.
I’ll admit it’s pretty weird that these models can write working code in a multi-language codebase but can’t add two four digit numbers. They’re almost savant-like the way they fail at basic tasks while excelling at more complex ones. But given that they are in fact excelling at complex tasks, I’m worried about what could happen if, say, every enterprise dev shop laid off 60% of their headcount and kept the remaining 40% just to do code review. That would be very bad for everyone in the industry.
- sciolist 4y agoAgreed. The article also speaks about engineers using these LLMs still as a tool - not necessarily replacing humans, but humans wielding them as tools. Copilot is a great example. Dinkum raised a great point about the models spitting out falsities or having other issues. We still will have and need human-in-the-loop engineering so that we can discern which outputs are true (or not)!
- dinkumthinkum 4y agoI’m not missing that. The point about Ramanujan is to show there is something fundamentally different and at a massive scale of difference between human intelligence and these AI systems. It’s not that average developers are math prodigies. It is generating code based on predictions from a large corpus of training data. How well does it do at debugging that code (assuming the bugs / code it finds are not the exact kinds posted to Web sites thousands times)?