Testing GPT 4's code-writing capabilities with some real world problems

The problem is, indeed, that Mr. Glaiel did not know the category of problem he was dealing with.

A correct statement would be: "Given a solution set containing both the shortest path through fire and the shortest path avoiding fire, select the solution that fits within six tiles of movement, preferring the solution that avoids fire where possible."

It's a constraint optimization problem in disguise: generate a solution set, then filter and rank the set to return a canonical result. That describes most of the interesting problems in gameplay code: collision and physics can use that framing, and so can most things called "AI". They just all have been optimized to the point of obscuring the general case, so when a gamedev first encounters each they seem like unrelated things.

The specific reason why it seems confusing in this case is because while pathfinding algorithms are also a form of constraint optimization, they address the problem with iterative node exploration rather than brute forcing all solutions. And you can, if you are really enterprising, devise a way of beefing up A* to first explore one solution, then backtracking to try the other. And it might be a bit faster, but you are really working for the paycheck that day when the obvious thing is to run the basic A* algorithm twice with different configuration steps. You explore some redundant nodes, but you do it with less code.

> Please elaborate.

I simply know firsthand (i'm old, i have manager, cto, ceo friends who I go golf and play squash with) that people in data entry and programming have been let go in the past weeks because 1 person could take over their work using the gtp/chatgpt api's and do their work faster with less errors. I am recommending the same in my company as a lot of my colleagues are doing nothing anymore as the skilled seniors are doing it themselves with gpt now as it's faster, less communication etc. We feed jira issues into gpt and it generates code; we review and refine or fix ourselves. It works much much faster and with better results. Most things most of us do all day is integrating ancient API's of partners and so mapping xml/soap/... api's to our json schema's. With chatgpt that's really fast and mostly painless; it even renames the properties that need to be changed to our enums properly. With humans this is a painful and slow process, especially with people who are fast and loose (broken education seems to made many of those graduate just by cheer production speed & volume instead of quality; gpt can do that better too...).

> so have you approached the media about this? If not, why not?

Why would I do that? Even anonymous, it doesn't seem to make much sense for me to do that. Anyway; that'll come soon enough as it will be common soon.

‘Understanding the business logic and getting it out of the customer’ is precisely what a lot of programmers are bad at doing. Many would rather talk to a compiler than a human being. For them, ChatGPT is a real existential threat.

Blah Blah Blah. I use ChatGPT for this every day to write code to save my own efforts and it is doing just fine thanks. I also use it for creative content in my apps, although I edit this work to get the tone in its writing correct. It is excellent for this.

The biggest thing here is that it's semi capable and improving. I feel safe about my job right now but it is worrying to invest time to compete with a machine that will continue to get better over the years where previously I felt safe that the effort of my labour would bear fruit for decades to come. Now I'm not so sure.