The document discusses how deep learning could automate cities by collecting various types of data from IoT sensors and using machine learning models. It provides examples of data that could be collected, describes the machine learning pipeline of data collection, feature extraction, and model learning. Potential use cases for deep learning in cities include predicting crime, optimizing transportation, analyzing franchise locations, predicting smog, alleviating traffic, better city planning, and better political policies.

1. How could Deep Learning
automate our cities?
Innovators’ Summit #4
Konrad Pabianczyk
Matthew Opala

2. What is deep learning?

4. Biological inspiration

7. 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

8. Machine Learning Pipeline
Data
Feature
Extraction
Learning

9. Machine Learning Pipeline
Data
Feature
Extraction
Learning

10. Machine Learning Pipeline
Data Learning

11. The brain’s visual system has 10.e14neural connections. And you only live for 10.
e9seconds. So it’s no use in learning one bit per second. You need more like 10.
e5bits per second. And there’s only one place you can get that much information:
from the input itself.
- Geoffrey Hinton

12. Shallow program
main
fun1
fun2
fun3
fun4
fun5
fun6

13. Deep program
main
fun1 fun2 fun3
fun4
fun5 fun6
fun8
fun9
fun7

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

15. 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

16. How do we learn representation?

17. Artificial Neural Net
input layer
hidden
layers
output layer

18. Fight Crime

20. 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

21. Training

22. 0,91 AUC

23. ROC Curve

25. A technological city is not a smart city

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