Infinite Craft
291–300 of 831 posts
Re: Infinite Craft
#292Can't believe I was the first to find "boobs". The 12 year-old combined "tomb raider" and "jiggle". LOL. It's actually a useful word. I can change anything male into female. Lots of other funny combinations: Burger King + Business = McDonalds Disney + Motive = Money Princess + Cleavage = Disney Plenty of political ones: President + Dictator = Trump White House + Flame = Trump Trump + Bribe = Impeachment Trump + Impea…
Re: Infinite Craft
#293Earlier quoted context omitted.
Forest + fire = smoke Mud + water = swamp Swamp + plant = Venus Flytrap Okay, okay. Venus Flytrap + Smoke = smoke detector
Tree + Water = river was also pretty baffling. Unfortunate. I can see the appeal of using an LLM for this but the results are pretty mediocre.
I laughed when "Vindaloo + Bubble" gave me "Burp", and "Burp + No Bacon" gave me "Sad"
Re: Infinite Craft
#294Re: Infinite Craft
#295Re: Infinite Craft
#296I got some funny ones, like: - Bread + Bread = Toast, + Fire = Burnt Toast, + Fire = Ash - Toast + Disaster = Pop Tart, + Fire = Hot Pocket, + Coffee = Coffee Pocket (First Discovery) - Hot Pocket + Cappuccino = Cappuccino Pocket (First Discovery), + Pop Tart = Cappuccino Pop Tart (First Discovery) - Coffee Pocket + Disaster = Coffee Spill (First Discovery), + Pop Art (sic) = Jackson Pollock - Jackson Pollock + Megad…
Re: Infinite Craft
#297Richest + Prostitute = Bill Gates
Bill Gates + Marriage = Divorce
Queen + Divorce = Behead
Trillionaire + Behead = Decapitalism (lol!)
Narcissist + Money = Politician
Peace + Trump = War
Re: Infinite Craft
#298Re: Infinite Craft
#299I was able to get some things like "Were-jesus-pope-hamster-crusader", "Trump-jesus-trump", "Pope Impaler", "Trump Impaler", "Vlad the Werehamster", "Richest were-genghis Khan", and also "Were-were-werewolf"!
I think the willingness of the language model to make up character names like this is a bit of a defect in this context, but maybe it's useful for brainstorming movie sequels. :-)
Re: Infinite Craft
#300Earlier quoted context omitted.
I thought they were using some kind of vector space searches like embedding.. no idea if that's the case
Yeah I'm pretty sure you could do this just with the classic word embeddings (king =queen + man - woman). Maybe it doesn't work as well as with a full LLM.
Associativity and commutivity in vector addition doesn't translate well to semantic meaning. Extrapolating your example, it'd also mean:
v_king - v_queen ~= v_father - v_mother
v_king - v_royalty ~= v_father - v_parent
v_king - v_father ~= v_royalty - v_parent ~= v_queen - v_mother
I don't see why those should all be true. Intuitively, trying to satisfy O(N^2) semantic pairings with vectors that are optimised for a very specific and different numerical operation (cosine similarity) feels like something that won't work. I'd imagine errors get amplified with 3+ operands.