Generative non-AI

In last week's episode of the Game Scoop podcast an idea was floated that modern computer game names are uninspiring and that better ones could be made by picking random words from existing NES titles. This felt like a fun programming challenge so I went and implemented it. Code and examples can be found in this GH repo.

Most of the game names created in this way are word salad gobbledigook or literally translated obscure anime titles (Prince Turtles Blaster Family). Running it a few times does give results that are actually quite interesting. They range from games that really should exist (Operation Metroid) to surprisingly reasonable (Gumshoe Foreman's Marble Stadium), to ones that actually made me laugh out loud (Punch-Out! Kids). Here's a list of some of my favourites:

• Ice Space Piano
• Castelian Devil Rainbow Bros.
• The Lost Dinosaur Icarus
• Mighty Hoops, Mighty Rivals
• Rad Yoshi G
• Snake Hammerin'
• MD Totally Heavy
• Disney's Die! Connors
• Monopoly Ransom Manta Caper!
• Revenge Marble
• Kung-Fu Hogan's F-15
• Sinister P.O.W.
• Duck Combat Baseball

I emailed my findings back to the podcast host and they actually discussed it in this week's show (video here starting at approximately 35 minutes). All in all this was an interesting exercise. However pretty quickly after finishing the project I realized that doing things yourself is no longer what the cool kids are doing. Instead this is the sort of thing that is seemingly tailor-made for AI. All you have to do is to type in a prompt like "create 10 new titles for video games by only taking words from existing NES games" and post that to tiktokstagram.

I tried that and the results were absolute garbage. Since the prompt has to have the words "video game" and "NES", and LLMs work solely on the basis of "what is the most common thing (i.e. popular)", the output consists almost entirely of the most well known NES titles with maybe some words swapped. I tried to guide it by telling it to use "more random" words. The end result was a list of ten games of which eight were alliterative. So much for randomness.

But more importantly every single one of the recommendations the LLM created was boring. Uninspired. Bland. Waste of electricity, basically.

Thus we find that creating a list of game names with an LLM is easy but the end result is worthless and unusable. Doing the same task by hand did take a bit more effort but the end result was miles better because it found new and interesting combinations that a "popularity first" estimator seem to not be able to match. Which matches the preconception I had about LLMs from prior tests and seeing how other people have used them.