How could Deep Learning automate our cities?

Innovators’ Summit #4

Konrad PabianczykMatthew Opala

What is deep learning?

Biological inspiration

IoT & Machine Learning● Temperature● Humidity● Seismic Activity● Lumens● Power Usage● Camera Feeds● Motion Sensors● Audio Recorders● Geolocation● Air Composition● Street View● Census Data

Types of Data● UVA/UVB● Ultrasound● Radiation● Precipitation● Wind● Traffic (People, Bikes, Cars, Buses...)● Parking● Noise● EM● Bluetooth● Trash● Crime

Machine Learning Pipeline

Data Feature Extraction Learning

Machine Learning Pipeline

Data Feature Extraction Learning

Machine Learning Pipeline

Data Learning

The brain’s visual system has 10.e14 neural connections. And you only live for 10.e9 seconds. So it’s no use in learning one bit per second. You need more like 10.e5 bits per second. And there’s only one place you can get that much information:

from the input itself. - Geoffrey Hinton

Shallow program

main

fun1

fun2fun3

fun4

fun5

fun6

Deep program

main

fun1 fun2 fun3

fun4

fun5 fun6

fun8fun9

fun7

But then I realised maybe that’s what hell is: the entire rest of eternity spent in f__g Bruges.

但后来我意识到也许这到底是什么：永恒的整个休息他妈的布鲁日花

How do we learn representation?

Artificial Neural Net

input layer

hidden layers

output layer

Fight Crime

What did they actually do?- gather data from open data sites of Chicago, San Francisco and some additional

data about weather and census

- preprocess data

- build one big matrix where rows correspond to crimes examples and columns to

some features like location, type of crime, area, date etc.

- split data to training, validation and test sets

- train Deep Neural Network to predict probability of arrest for a given crime

Training

0,91 AUC

ROC Curve

A technological city is not a smart city

Use Cases

● Predicting Crime● Optimizing Transportation● Analyzing expected ROI of a franchise location● Predicting Smog● Alleviating Traffic● Better City Planning● Better Political Policies