Neural networks are a type of machine learning program that learns from examples they’re given, rather than relying on a human programmer to invent rules.

In an earlier experiment, I trained a neural network to write new names for Dungeons and Dragons spells based on a list of 365 examples. That’s a really small dataset for a neural network to work with, and I ended up struggling to find training parameters that would strike a balance between word-for-word mimicry of the original list of spells, versus a series of completely made-up words. By filtering extensively through the nonsense, I was able to come up with a short list of interesting new spells. (My favorites were Barking Sphere and Gland Growth).

However, blog reader Jo Scott was kind enough to collect the entire 4th edition list of spells - more than 1,300 spells in all. She explained that she’s playing a character who’s an artificer trying to create an autonomous spellcasting golem - essentially, a magical AI - and she’d like to have more weird spells for the golem to invent. (Her Dungeon Master okayed this and thus only has herself to blame when she has to deal with some of the spells listed below.)

Using the new dataset I was able to train a much better-performing neural network. It simply had many more examples of spells to work with; that is, more examples of the words and letter combinations that appear in D&D spells, and thus was able to deduce better rules about how to create them.

For comparison, here’s what the neural network trained on the original spell dataset was producing after it had looked through the spell list 30 times. This is raw, unfiltered output from the neural network.

Original dataset

Wome on frr
Eser Wold
Lelent Warder
Cleater Secfen
Spiritul Plage
Speak with Alanc
Plonting Cloud
Stige Dling
Comenthon of Prost
Resser RestractiGn
Bline Ons
Dood to Stone

Aside from a couple of spells that just might work, most of the list is magicky-sounding nonsense, sometimes barely pronounceable.

By contrast, this is what the neural network was producing after it had been trained on the dataset that included all the 4th edition spells:

Full dataset

Curse Word
Crackling claus
Tidal treket
Swirk with
Wall of Storm
Acter Lor distertion
Glib ton
Grasping Mane
Tweel Strike
Revitalizing Strike
Truneming fortune
Fall of the Wild
Trickstrak empester
Phantasmal assault
Tidalt Atight
Leging Blade
Bund Wind
Dance of Sack
and Prime
Mass Cure Fortion

They’re not ALL winners, but the difference is dramatic. This is why, although I can often have fun with small datasets, the really large ones (100,000+ metal bands, or 19,000 IPA beers) tend to produce the most consistently convincing results.

Even this more-sophisticated neural network is not without some oddities. For example, you’ll notice in the results below that it seems to have a particular fondness for bears. And it has invented the name “Dave” which is now, for some reason, its favorite.

I leave you with a selection of Dungeons and Dragons spells generated by the latest neural network.

Mister of Light
Storm of the gifling
Song of goom
Forceful Boor
Chorus of the dave
Maine storm
Frames of Death
Song of the doom goom
Death’s Death’s Proud Bear
Wall of Distraction
Date wards
Plant of Peace
Shield of Farts
Song of the darn
Ward of Snade the Pood Beast
Ice shop
Primal Rear
Summon Storm Bear
Divine Boom
Soul of the bill
Charm of the dave
Spirit of the Spirit
Fire shop
Song of blord
Song of distraction
Forceful Force
Spirit Boating
Song of the ball
Hail to the Dave
Crusading Disk
Summon ass
Call to the Daring
Treeking of Star
Grasping Light
Clinging blade
Primal Prayer Bear
War Cape
Find Strike
Song of the Unworthy
Gate Sail
Icon of Thorns
Song of the door
Star warper
Stone of Death
Chilled arrow
Storm of the dave
Fark Mate
Charm of the cods
Death of the Sun
Greater flick
Curse Clam
Claming Blow
Cursing wink
Conjure Mare
Conjure Bark
Darkworm Colt
Daving fire
Healing of Bat
Mordenkainen’s lucubrabibiboricic angion

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