We decided at work to run a little experiment with GPT3 to see if/how-much it was 'worth it'.
Since baseball is back and most of us are fans, we decided to write a baseball simulator. We each had a Friday afternoon to write one up. Half of us got to use the free GPT3, and half had just regular googling. After the jam, we'd compare notes at the bar and see what the difference, if any, was.
Holy cow, was there ever a difference.
Those without GPT3 got pretty far. Got the balls and strikes and bases and 9 innings. Most got extra innings down. One even tried the integration with ERA and batting stats in the probabilities of an event occurring but was unable to get it done.
The GPT3 group was estimated to be 2 weeks worth of work ahead of the googling group. Turns out, there is a whole python library for baseball simulations and statistics. The googling group didn't find that, but GPT3 just prompted it outright on the first query for everyone using it. This group got the basics of the game done in ~30 minutes. Managed to get integration with actual MLB statistics. Built somewhat real physics simulators of balls in play and distances, adjusted for temperature and altitude. Not all of them at once, but a lot of really great stuff.
Aside: Did you know that MLB publishes, in real time, all 6 degrees of freedom for a ball, from where it leaves a pitchers hand to where a catcher/batter interacts with it? They put out the spin rates in three axes! Wild stuff.
Our conclusions were that it's totally 'worth it' and is a ~20x multiplier in coding speed. It spits out a lot of really bad code, but it gets the skeletons out very quickly and just rockets you to the crux of the problems. For example: it gave out a lot of jibberish code with the python baseball library; like trying to pass a date into a function that only takes in names. But it gives you the correct functions. Easy enough to go and figure out the documentation on that function.
Like I said, it's a ~20x multiplier for our little experiment.
Action Items for management: Pay whatever you have to and let us use it all the time.
So how would GPT fare at writing a simulation for a problem ... that has no source code or even literature for it in the (crawlable) public domain?
Also, as to what the GPT group was able to produce -- sure it was a lot of code, and apparently a quite a bucket of features -- but did it actually produce a usable simulation? Or even a coherent statement of what a "baseball simulation" should do, actually, and how its accuracy is to be measured?
I'm not casting aspersions here - I'd really like to know.
It does not produce a usable simulation of baseball right out of the box. It'll give you skeleton code that you kinda have to then fill in yourself. But it's really good skeleton code. Like, the functions are used wrong, but it's the correct function. The explanation of the code that it give you is really spot on though. Like, yes, those are the correct steps a coder should implement.
It's easy enough to try it out for yourself too! Give yourself a challenge and see where it takes you.
I'll definitely give it a whirl sometime. And I appreciate the detailed field report.
It's just that, if someone gave me 3 hours, and asked me to come back with constructive, actionable progress toward creating a simulator for X (where X is sufficiently rich and complex, like baseball) -- I wouldn't mess around with skeleton code at all.
Instead I'd try my best to come up with a statement of what the simulator should do, and why.
Yeah, in our case it was baseball and most of us are fans. So, we all knew what to do and what 'good' looked like. It was still pretty open ended though, which was fun. It was good to see what my coworkers came up with and the different approaches taken.
Since baseball is back and most of us are fans, we decided to write a baseball simulator. We each had a Friday afternoon to write one up. Half of us got to use the free GPT3, and half had just regular googling. After the jam, we'd compare notes at the bar and see what the difference, if any, was.
Holy cow, was there ever a difference.
Those without GPT3 got pretty far. Got the balls and strikes and bases and 9 innings. Most got extra innings down. One even tried the integration with ERA and batting stats in the probabilities of an event occurring but was unable to get it done.
The GPT3 group was estimated to be 2 weeks worth of work ahead of the googling group. Turns out, there is a whole python library for baseball simulations and statistics. The googling group didn't find that, but GPT3 just prompted it outright on the first query for everyone using it. This group got the basics of the game done in ~30 minutes. Managed to get integration with actual MLB statistics. Built somewhat real physics simulators of balls in play and distances, adjusted for temperature and altitude. Not all of them at once, but a lot of really great stuff.
Aside: Did you know that MLB publishes, in real time, all 6 degrees of freedom for a ball, from where it leaves a pitchers hand to where a catcher/batter interacts with it? They put out the spin rates in three axes! Wild stuff.
Our conclusions were that it's totally 'worth it' and is a ~20x multiplier in coding speed. It spits out a lot of really bad code, but it gets the skeletons out very quickly and just rockets you to the crux of the problems. For example: it gave out a lot of jibberish code with the python baseball library; like trying to pass a date into a function that only takes in names. But it gives you the correct functions. Easy enough to go and figure out the documentation on that function.
Like I said, it's a ~20x multiplier for our little experiment.
Action Items for management: Pay whatever you have to and let us use it all the time.