Re:Google DeepMind
by r3trohack3r on Thursday April 20, 2023 @06:33PM
(#35647430)
I guess my response to that is: so what?
Regardless of the truthiness of your assertion, that general description of work is a majority of my profession. Novel contribution is a very small percentage of the work I do. I'm looking forward to that shifting significantly in the coming years.
It doesn't matter if the "POSIX compliant C" version of dirbuster was in it's training set. Or if my cloudflare wrangler JS API <-> Neon.tech SQL database implementation was. (Copyright arguments withstanding for sure.)
What matters is that it did 100% of the work of generating the code. All I did was guide it, code review it, and ship it. It felt like I had a team of junior engineers working in the trenches on the problem for me and I was getting to have fun playing at a level up the stack. It can generate 100LoC faster than I can type it, and I can code review it as it generates it. I can fix the code up in the editor and paste it back in with the next prompt for another round of work.
This is with the current model.
Unlike GP's assertion that people are falling for marketing, I actually don't know what OpenAI's marketing is. 100% of my belief in GPT-4 comes from it doing my real day-to-day tasks along side me. I don't need a crystal ball to predict what AI is going to do, I'm already using it as a partner in writing my code. It's already well enough adept at my profession to pair-program through problems with me.
This week I had a particularly hairy problem I needed to solve, and I was already behind schedule on the project. ChatGPT and I knocked it out in ~30 minutes. I suspect, if I were alone, it would have taken me several hours. The velocity boost came from: ChatGPT correctly identifying it as a binpacking problem (which I had missed), listing out several algorithms for approaching binpacking, and giving me an initial (incorrect) first implementation. I was able to go back and forth on that response and get the rough constraints figured out for a solution, ask it to generate that solution, and then clean it up in my editor.
The flow I'm using today is already a 100% increase in effective "workforce" for a software organization with a $20 per month per employee subscription to ChatGPT. Everyone gets a real-time, always available, pair programmer.
Regardless of the truthiness of your assertion, that general description of work is a majority of my profession. Novel contribution is a very small percentage of the work I do. I'm looking forward to that shifting significantly in the coming years.
It doesn't matter if the "POSIX compliant C" version of dirbuster was in it's training set. Or if my cloudflare wrangler JS API <-> Neon.tech SQL database implementation was. (Copyright arguments withstanding for sure.)
What matters is that it did 100% of the work of generating the code. All I did was guide it, code review it, and ship it. It felt like I had a team of junior engineers working in the trenches on the problem for me and I was getting to have fun playing at a level up the stack. It can generate 100LoC faster than I can type it, and I can code review it as it generates it. I can fix the code up in the editor and paste it back in with the next prompt for another round of work.
This is with the current model.
Unlike GP's assertion that people are falling for marketing, I actually don't know what OpenAI's marketing is. 100% of my belief in GPT-4 comes from it doing my real day-to-day tasks along side me. I don't need a crystal ball to predict what AI is going to do, I'm already using it as a partner in writing my code. It's already well enough adept at my profession to pair-program through problems with me.
This week I had a particularly hairy problem I needed to solve, and I was already behind schedule on the project. ChatGPT and I knocked it out in ~30 minutes. I suspect, if I were alone, it would have taken me several hours. The velocity boost came from: ChatGPT correctly identifying it as a binpacking problem (which I had missed), listing out several algorithms for approaching binpacking, and giving me an initial (incorrect) first implementation. I was able to go back and forth on that response and get the rough constraints figured out for a solution, ask it to generate that solution, and then clean it up in my editor.
The flow I'm using today is already a 100% increase in effective "workforce" for a software organization with a $20 per month per employee subscription to ChatGPT. Everyone gets a real-time, always available, pair programmer.