|
|
|
|
|
by abhaynayar
1187 days ago
|
|
The technology hadn't clicked for me either. Today I had to write a script for which it would have taken me maybe 30 minutes or so on my own. I asked ChatGPT (GPT-4) to write it for me, and it got it right in the first try. I just spent a few minutes checking over the code. It truly is magical when the code just runs. Later I asked it to make several non-trivial changes to the code based on more requirements I thought of, and it aced those on the first go as well. Again, I checked the code for a negligible amount of time - compared to how much it would have taken me to write the code on my own. I do think humans will slowly get worse at lower-layers of the computer stack. But I don't think there's anything inherently bad with it. Compilers are also doing the work for you, and they are making you bad at writing assembly code - but would you rather live in a world where everyone has to hand-write tedious assembly-code? Maybe, in the future, writing Python would be like what writing assembly is today. We might go down the layer-cake once in a while to work with Python code. That does not mean we give up on the gains we get from whatever layers are going to be put on top of Python. |
|
What is the equivalent of this for LLMs? Is there anyway generative models can give a guarantee that this prompt will 100% translate to this assembly? As far as I understand, no. And the way autoregressive models are built I don't think this is possible.
I agree that they are useful for one-offs like you said, and their ability to tailor the solution for your problem (as opposed to reading multiple answers on stackoverflow and then piecing it yourself) is quite deadly, but for anything that is even slightly consequential, you are going to have to read everything it generates. I just can't figure out how it integrates into my workflow.