Training an AI to generate FixMyStreet reports

Artificial intelligence and machine learning seem to be everywhere at the moment – every day there’s a new story about the latest smart assistant, self-driving car or the impending take over of the world by robots. With FixMyStreet having recently reached one million reports, I started wondering what kind of fun things could be done with that dataset.

Inspired by a recent post that generated UK place names using a neural network, I thought I’d dip my toes in the deep learning sea and apply the same technique to FixMyStreet reports. Predictably enough the results are a bit weird.

I took the titles from all the public reports on as the training data, and left the training process to run overnight. The number crunching was pretty slow and the calculations had barely reached 5% in the morning. I suspect the training set was a bit too large, at over 1M entries, but end result still gives enough to work with.

The training process produces checkpoints along the way, which you can use to see how the learning is progressing. After 1000 iterations the model was starting to be aware that it should use words, but didn’t really know how to spell them:

Ice does thrown campryings
Sunky riking proper, badger verwappefing cars off uping is!
Finst Knmp
Lyghimes Jn fence
Moadle bridge is one descemjop

After 15000 iterations it’s starting to get the hang of real words, though still struggling to form coherent sentences.

Untaxed cacistance.
Broken Surface in ARRUIGARDUR. Widdy movering
Cracked already nail some house height avenue.
Light not worky
I large pot hole
Dumped shood road nod at street.
Dog man
Ongorently obstructing sofas. This birgs.
Serious Dirches

After 68000 iterations there seems to be enough confusion in the training data that things start to go south again with the default parameters:

Urgely councille at jnc swept arobley men.
They whention to public bend to street? For traffic light not working

Tweaking the ‘temperature’ of the sampling process produces increasingly sensible results:

Large crumbling on pavement
Potholes all overgrown for deep pothole
Very van causing the road
Very deep potholes on pavement
Weeds on the pavement
Several potholes in the road
Rubbish Dumped on the road markings
Potholes on three away surface blocking my peride garden of the pavement
Potholes and rubbish bags on pavement
Poor road sign damaged
Poor street lights not working
Dog mess in can on road bollard on pavement
A large potholes and street light post in middle of road

As well as plenty of variations on the most popular titles:

Pot hole
Pot hole on pavement
Pot holes and pavement around
Pot holes needings to path
Pothole dark
Pothole in road
Pothole/Damaged to to weeks
Potholes all overgrown for deep pothole
Potholes in Cavation Close
Potholes in lamp post Out
Potholes in right stop lines sign
Potholes on Knothendabout
Street Light
Street Lighting
Street light
Street light fence the entranch to Parver close
Street light not working
Street light not working develter
Street light out opposite 82/00 Tood
Street lights
Street lights not working in manham wall post
Street lights on path
Street lights out

It also seems to do quite well at making up road names that don’t exist in any of the original reports (or in reality):

Street Light Out - 605 Ridington Road
Signs left on qualing Road, Leave SE2234
4 Phiphest Park Road Hasnyleys Rd
Apton flytipping on Willour Lane
The road U6!

Here are a few of my favourites for their sheer absurdity:

Huge pothole signs
Lack of rubbish
Wheelie car
Keep Potholes
Mattress left on cars
Ant flat in the middle of road
Flytipping goon!
Pothole on the trees
Abandoned rubbish in lane approaching badger toward Way ockgatton trees
Overgrown bush Is broken - life of the road.
Poo car
Road missing
Missing dog fouling - under traffic lights

Aside from perhaps generating realistic-looking reports for demo/development sites I don’t know if this has any practical application for FixMyStreet, but it was fun to see what kind of thing is possible with not much work.

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Image: Scott Lynch (CC by/2.0)

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